Disasters occur over a short or long period of time and cause large-scale harm to humans, infrastructure, as well as the ecosystem every year. Immediate response after a disaster helps minimize its impact on life and property. Therefore, it is crucial to have an emergency response system ready to handle any emergency that may come up after a disaster. In this paper, a model is proposed to optimize the distribution of emergency services at disaster-struck points. Due to the NP-hardness of the problem, two metaheuristic algorithms, Particle Swarm Optimization and Cuckoo Search Optimization have been used to dynamically allocate the available resources based on the given situation. The proposed model uses the distance between the emergency location and the emergency service provider, and the severity of the emergency as the main metrics for scoring any considered solution. The conducted experiments demonstrate that the model provides effective, efficient, and dynamic allocation service at emergency locations in simulated disaster situations.