Abstract: The efficiency of emergency supply distribution is a core factor influencing the effectiveness of post-disaster relief. However, road damage caused by sudden disasters severely constrains the efficiency of traditional ground transportation. Consequently, coordinated delivery employing Unmanned Aerial Vehicles (UAVs) and trucks has been widely adopted in emergency scenarios such as post-disaster relief and medical supply distribution. To optimize supply distribution efficiency in disaster scenarios, this paper focuses on a truck-UAV coordinated delivery mode. According to the varying accessibility of villages, a coordinated distribution model is established, where trucks serve as mobile depots and UAVs perform multi-sortie operations. With the objective of minimizing the total task load, including truck travel time, UAV flight time, and mutual waiting time, a mixed-integer programming model is formulated, incorporating constraints such as vehicle capacity, UAV payload, and task time windows. This study relaxes the traditional constraint that UAV can only take off and land at fixed nodes, allowing UAVs to perform takeoff, landing, and replenishment at any point along the truck route, thereby enabling in-route replenishment. To solve the model, a two-stage hybrid heuristic algorithm is designed. In the first stage, a genetic algorithm is employed to optimize the basic travel routes of both trucks and UAVs as well as their rendezvous points. In the second stage, dynamic programming is applied to determine the optimal assignment of delivery tasks between trucks and UAVs for villages. Numerical experiment results demonstrate that the proposed "en-route replenishment" strategy significantly reduces both UAV flight time and mutual waiting time by at least 8.3% and up to 31.72%. Compared with the "village-node-only replenishment" strategy, this study provides a scheduling solution that balances timeliness and feasibility, thereby extending the theory of coordinated optimization in emergency management.