The "last mile" distribution of rural logistics has always been considered the key to optimizing logistics efficiency and cost. With the rapid increase in the demand for logistics and distribution in rural areas, a site-distribution integration optimization model that combines trucks and drones was proposed, aiming to maximize the coverage of demand points while minimizing the total distribution cost by optimizing the site selection of drone distribution centers, truck route planning, and drone task allocation. Based on this collaborative distribution model, a genetic algorithm combined with an immune optimization mechanism was applied to form a multiple nested genetic algorithm for solving the problem. The feasibility of the proposed model in rural logistics scenarios was verified through simulation experiments, and an optimal global distribution scheme was identified by taking Fuliang County in Jingdezhen City as an example. Additionally, sensitivity analysis is conducted, demonstrating that the model is effectively adaptable to the parameter settings of different UAV types. The three selected commercially available logistics UAVs are observed to exhibit good convergence effects in actual operations, with convergence values significantly better than the traditional assumption conditions.