This paper investigates an unmanned aerial vehicle (UAV)-enabled wireless sensor network, which simultaneously achieves data collection and energy transmission during hovering periods utilizing a full-duplex hybrid access point mounted on the UAV. In order to maximize the energy efficiency of UAV while extending the life cycle of the network, the trajectory control of the UAV is constructed as a dynamic multi-objective optimization problem. Three key indicators: the sum data rate, total harvested energy and the UAV’s energy consumption is considered simultaneously to improve overall quality of service of the network by balance the power consumption and the data collection. An adaptive reference vector-based dynamic multi-objective stochastic fractal search algorithm is introduced to solve the dynamic trajectory optimization problem. Since target device selection is the core factor directly affect the UAV’s trajectory, a data-buffer priority strategy is proposed to guide the hovering. In this way, the data loss caused by data overflow can be minimized on the basis of ensuring the priority of data transmission. On this basis, a dimensional Gaussian walk strategy and an adaptive step size generator is designed in search process to effectively enhance the exploration performance of the proposed algorithm. Simulation experiments show that, compared with other algorithms, the proposed algorithm performs better in enhancing the sum data rate and total harvested energy, while significantly reducing the UAV’s energy consumption, effectively improving the overall system performance.