Objective: The large-scale adoption of electric vehicles (EVs) has become a critical strategy for decarbonizing the transportation sector. However, uncoordinated EV charging behavior widens the peak–valley load difference of distribution networks compared with the base load without EVs, posing formidable challenges to grid operation and security. Studies on EV charging scheduling optimization overlook the effective coupling between carbon factors and electricity prices, preventing carbon costs from guiding user charging behavior through price. Methods: To address the above issue, this study proposed a dynamic pricing-based multi-objective EV charging scheduling model that incorporates carbon price and a carbon pass-through coefficient. The model considered the grid load peak–valley difference, operator revenue, and user charging cost as the objectives. Meanwhile, regret theory was adopted to characterize boundedly rational user decision-making behavior in choosing charging time periods and charging stations. To address the high-dimensional nonlinearity and multi-constraint characteristics of the model, as well as the tendency of existing solution algorithms to fall into local optima, this study developed adaptive weighting and disturbance factors based on the crested porcupine optimizer (CPO) to solve the above multi-objective nonlinear optimization problem. Taking the Nanshan District of Shenzhen city, China, as the case study with the IEEE 33 bus system, simulation experiments were conducted to validate and calibrate the rationality of the proposed dynamic pricing mechanism, the superiority of the improved CPO (ICPO), and the patterns of carbon price and carbon pass-through coefficient on the optimization performance of EV charging scheduling. Results: The results of this study were as follows. 1) The influence of carbon price and carbon pass-through coefficient on the multi-objective optimization of the system exhibited remarkable threshold effects, with a critical threshold identified at a carbon price of 70 RMB/t and a carbon pass-through coefficient of 0.3. Below this threshold, the peak–valley load difference remained relatively small, and user costs were low. Above this threshold, the user costs increased substantially, and the peak–valley load difference increased in some scenarios. The optimal parameter combination was a carbon price of 60 RMB/t and a carbon pass-through coefficient of 0.2, which achieved synergistic optimization between grid security and user benefits. 2) The ICPO demonstrated excellent optimization performance under the proposed carbon–electricity coordinated scheduling strategy. Compared with the genetic algorithm, particle swarm optimization, and the original CPO, the ICPO reduced the peak–valley load difference by 27.5%, 19.2%, and 18.4%, respectively. It also reduced user charging costs by 5.6%, 16.4%, and 6.6%, respectively. Furthermore, the final solution quality is improved by 14.9%, 10.3%, and 9.4%, respectively. 3) The number of charging users and user satisfaction exhibited a remarkable unimodal distribution pattern as the regret weight combination varied. Within the price regret weight range of 0.4–0.6, both indicators remained at relatively high levels, demonstrating a stable superior performance zone. Conclusions: The dynamic pricing model developed in this study can more effectively guide users toward coordinated charging behavior. The results demonstrate that an appropriate combination of carbon price and carbon pass-through coefficient enables the synergistic optimization of grid security and user benefits. The ICPO exhibits superior performance in reducing peak–valley load difference and user charging costs, highlighting the effectiveness of the proposed adaptive weighting and disturbance factor strategies in high-dimensional, multi-constraint optimization scenarios. A moderate configuration of regret weights is crucial for balancing user participation and system scheduling performance, whereas extreme weight settings significantly suppress users' willingness to charge and reduce satisfaction. This study provides scientific support for government carbon market policy design and for electricity pricing strategies by system operators. Future research could extend the proposed framework by incorporating a coupled transportation–distribution network model to jointly optimize travel routes and charging scheduling, thereby further enhancing its practical applicability.