Objective: Increasing complexity of disaster rescue systems and frequent global natural disasters require efficient emergency command mechanisms to reduce life and property losses. Large-scale earthquakes present time-sensitive, multi-dimensional challenges, needing rapid decisions, precise resource allocation, and cross-departmental coordination. However, current research does not quantitatively analyze how emergency command capabilities affect rescue efficiency in dynamic disaster scenarios. This study develops a multi-agent simulation model based on the 6.2-magnitude Jishishan earthquake in China to assess the impact of command strategies on rescue operations and optimize emergency response systems. Methods: A multi-agent simulation model is developed using the NetLogo platform to meet the research objectives. It represents the emergency command and rescue system with four types of agents: the on-scene chief command agent, the on-scene support command agent, the emergency rescue force agent, and the disaster-affected agent. Each agent has specific behavioral patterns and interaction rules. The on-scene chief command agent oversees coordination, decision-making, and resource allocation. The on-scene support command agent manages task planning, resource scheduling, and real-time information feedback. The emergency rescue force agent performs rescue tasks, while the disaster-affected agent represents victims awaiting rescue. The simulation model is designed to reflect real-world scenarios, focusing on key variables such as information completeness, decision-making capability, resource allocation efficiency, and coordination success rate. This study analyzes scenarios under different conditions: (1) Information incompleteness: limited communication and fragmented data; (2) Resource scarcity: imbalanced demand-supply distribution; and (3) Feedback delays: lagging information updates and decision adjustments. The rescue rate (R), defined as the ratio of rescued victims to total victims, is the primary performance metric. Comparative analyses adjust agent capabilities to identify optimal strategies. Results: The simulation results highlight key findings: (1) Critical role of command capabilities. The on-scene chief command agent's information organization and coordination control capabilities are crucial in accelerating early-stage rescue operations. When optimized, these capabilities increase R by 0.4 within the first five simulation ticks. The on-scene chief command agent's feedback adjustment capability becomes crucial in later stages, thus reducing task conflicts by 0.25 through dynamic strategy updates. (2) Scenario-specific optimization strategies. Under incomplete information conditions, improving the on-scene support command agent's resource scheduling speed increases R from 0.4 to 0.9 in 9 ticks. During resource scarcity, enhancing the on-scene support command agent's coordination ability minimizes allocation conflicts, thus achieving a stable R of 0.7 despite limited supplies. During feedback delays, enhancing the on-scene support command agent's task prioritization management reduces decision latency by 30%, thus increasing R from 0.5 to 0.68 in 12 ticks. (3) Role of lower-level command agents. This study emphasizes the significance of lower-level command agents, especially the on-scene support command agent, in enhancing rescue efficiency. Optimizing their resource scheduling and coordination abilities can significantly enhance the overall rescue operation, even under complex, challenging conditions. Conclusions: This study quantitatively confirms that effective emergency command is crucial for earthquake rescue efficiency. The on-scene chief command agent's information integration and macro-level coordination capabilities form the foundation for rapid response, while the on-scene support command agent's strategic optimizations are critical under resource constraints. A hierarchical, decentralized command structure is recommended to effectively balance decision-making authority with operational flexibility. Future research should combine dynamic disaster factors to evaluate the robustness of command strategies in unpredictable scenarios.