The use of unmanned aerial vehicles (UAVs) for long-range energy replenishment and data collection in wireless sensor networks (WSNs) across various scenarios seeks to minimize energy consumption by optimizing the scheduling and visiting sequence of the UAV’s hovering points. The problem can be modeled as a joint scheduling and trajectory optimization problem (JSTOP). To address the challenges posed by the limitations of the traditional JSTOP algorithm, we propose a multi-objective optimization algorithm-based joint scheduling and trajectory optimization scheme (MOJSTOP), which consists of two sub-algorithms, the improved multi-objective simulated annealing algorithm (Improved MSA) and the improved non-dominated sorting genetic algorithm II (Improved NSGA-II). The Improved MSA algorithm is well-suited for small-scale scenarios. In contrast, the Improved NSGA-II algorithm is designed for large-scale scenarios where the UAV has limited energy capacity. In addition, it introduces a new optimization objective that focuses on the energy utilization efficiency of the UAV. The simulation results demonstrate that the proposed MOJSTOP scheme exhibits superior computational efficiency, convergence, and stability in solving the JSTOP problem compared to other algorithms and is applicable to various terrains.