Earthquakes rank among the most devastating natural disasters, causing profound harm to communities and ecosystems. Their impacts extend beyond physical destruction, leading to economic losses and significant human suffering. In the aftermath, collapsed infrastructure and widespread injuries create an urgent need for medical care, food, and other essentials. Humanitarian aid logisticseffectively in such scenarios is complex, as it requires addressing the spatial distribution of affected areas, and ensuring rapid response. This study tackles these challenges by addressing the allocation and routing in humanitarian aid logistics for earthquake relief in the west of Tehran’s Region 4. A depot-aware Genetic Algorithm (GA) cluster-seeded initialization is employed to solve the allocation and routing of aid as a Capacitated Multi-Depot Vehicle Routing Problem (CMDVRP), designing efficient routes for aid distribution across 649 demand points using 2 depots, with each depot deploying 6 vehicles of 12,800-unit capacity. The GA solution achieves a total distance of 297372.159 meters across 12 routes, successfully serving all customers. By focusing on spatial analysis and route efficiency, this work contributes to GIS-driven disaster response strategies.