The growing adoption of unmanned aerial vehicles (UAVs) for urban parcel delivery highlights the need for efficient distribution center placement. Particularly in dense urban environments, obstacle avoidance usually increases real path distances beyond straight-line measurements. Considering these distance differences, this study first employs the Informed-RRT* algorithm with spatial indexing to calculate real UAV flight paths between locations. We use the real path distances instead of straight-line distances to determine service coverage areas in the distribution center layout model. In this model, we aim to minimize the total economic cost and maximize customer satisfaction, considering the service range, the balance between delivery capability and demand, and the constraints of the distribution mode. An immune-algorithm-enhanced genetic algorithm, the immune genetic algorithm, is used to solve the model, acquiring the distribution center layout. We conduct a simulation experiment in Shenzhen’s low-altitude airspace and compare the layout results between the real path distance criterion and the straight-line path distance criterion. The results show that using the real path distances instead of the straight-line distances leads to changes in distribution center layout, demonstrating the necessity of this framework.