Emergencies can cause complex challenges for emergency hospitals, requiring an effective allocation of limited resources to reduce the loss toll. In this paper, we address a declarative modeling approach for the problem of assigning casualties to hospitals in an agent-based simulation context for emergency crises. Our approach leverages autonomous and decision-making-capable agents to optimize resource allocation while addressing various constraints, including but not limited to resource shortages and patient prioritization criteria. In addition, we present simulation outcomes that illustrate the effectiveness of the proposed approach in various randomly generated mass casualty incident scenarios. Furthermore, we conclude that our approach offers a promising direction to increase the efficacy and resilience of the rescue team’s responses in the face of mass casualty incidents.