Abstract: The efficiency of emergency material distribution is a core factor influencing the effectiveness of post-disaster relief operations. However, road damage caused by sudden disasters severely constrains the efficiency of traditional ground transportation. Consequently, the coordinated delivery model employing Unmanned Aerial Vehicles (UAVs) and trucks has been widely adopted in emergency scenarios such as post-disaster rescue and medical supply distribution. To optimize material scheduling efficiency in disaster scenarios, this paper focuses on a truck-UAV cooperative delivery mode. Accounting for the varying accessibility of villages, a coordinated distribution model is established, where a truck serves as mobile depots and a UAV perform multi-sortie operations. Aiming to minimize the total task load—comprising 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 restricts UAV launch and recovery to fixed nodes, allowing operations at any point along the truck's route, thereby enabling in-route replenishment. To solve the model, a two-stage hybrid heuristic algorithm is designed: the first stage employs a genetic algorithm to optimize the base routing paths and rendezvous points for both the truck and UAV, while the second stage utilizes dynamic programming to determine the optimal assignment of villages to either UAV or the truck. Numerical results demonstrate that the proposed "in-route replenishment" strategy significantly reduces both UAV flight time and mutual waiting time, with decreases ranging from at least 8.3% to a maximum of 31.72%. By comparing with the "village-node-only replenishment" strategy, this study provides a scheduling decision-making solution that balances timeliness and feasibility, thereby extending the theory of cooperative optimization in emergency management.