In the unmanned aerial vehicle (UAV) inspection operation, heterogeneous UAVs often face coordination and nest site selection problems due to their different functions and range capabilities. The optimal deployment strategy of the UAV nest can be seen as a new type of location optimization problem. Compared with the traditional facility location problem, the deployment of the UAV nest is facing more new challenges. This paper comprehensively uses geographic information systems and TOPSIS method to pre-screen candidate locations, and then uses a combination of greedy algorithms and Lagrange relaxation optimization of the p-median coverage problem optimization method. After comprehensively considering factors such as node placement principles, flying tasks, flying radius, and functional redundancy, a multi-objective optimization lowest-cost UAV nest location method is proposed. The nest distribution problem is defined as a p-median problem with the lowest cost under pre-selected restricted factors, and principal constraints are set to achieve multi-objective optimization lowest-cost node placement and reduce inspection costs from multiple perspectives. The experimental results show that the cost savings of the nested distribution after multi-objective optimization are more than 9.2% compared with those of traditional methods in terms of construction, maintenance, inspection, and comprehensive costs.