In recent years, the use of mobile charging vehicle (MCV) to simultaneously replenish energy for multiple nodes has become a research focus in wireless rechargeable sensor networks (WRSNs). The selection of charging thresholds is crucial to enhance the charging efficiency of WRSNs. However, most of the existing work uses a fixed threshold, which has to face the risk of frequent charging triggers when the threshold is too high or data loss when the threshold is too low. Furthermore, this challenge will become even more severe in the fact that the charging queue changes. Therefore, this article first proposes an adaptive dual-threshold online selection algorithm (ADT-OSA) based on the distance between nodes, energy consumption rate, and remaining energy to determine upper and lower charging thresholds for each node. Subsequently, an improved real-time genetic algorithm (IR-IGA) is introduced based on the overall and real-time energy situation of WRSNs to dynamically determine the charging path for MCV. Finally, the performance of the two algorithms is evaluated through extensive simulations. The results show that the adaptive double threshold by the ADT-OSA algorithm is better than the double threshold obtained by the trial and error method, and the IR-IGA algorithm significantly reduces the mobility energy consumption, charging energy consumption, and the amount of data loss compared with existing methods.