<p>The use of unmanned aerial vehicles (UAVs) for transmission tower inspection has gained increasing attention given their potential to enhance operational efficiency, reduce costs, and improve safety. This paper explores a novel UAV location-routing problem that arises in transmission tower inspection. Specifically, the problem involves determining the station locations, UAV routes between transmission towers, and inspection paths around each transmission tower. We first propose a mixed-integer linear programming model to formulate this problem. As the problem is computationally challenging, we introduce a hybrid evolutionary algorithm based on a memetic computing framework. The proposed algorithm includes a pre-processing procedure to accelerate search efficiency, an inherit-repair crossover operator to generate high-quality offspring solutions, a mixed tabu strategy for intensified local improvement, and a quality-and-distance-based population updating mechanism to preserve solution diversity. Extensive computational experiments based on 70 real-world instances demonstrate the effectiveness of the proposed algorithm. The results show that our algorithm exhibits competitive performance against an exact solver on small instances, and it significantly outperforms other algorithms being compared on large instances. These findings provide a valuable benchmark for future research on UAV-based infrastructure inspection optimization.</p>