<p>Effectively managing energy in wireless sensor networks is crucial for maintaining network stability, as the depletion of sensor node energy can result in partial network breakdowns, impaired communications, incomplete data, delayed responses to emergencies, which could lead to catastrophic consequences. Previous research focused on designing routes through which mobile chargers can replenish the energy of sensor nodes based on their urgency by considering travel distances and the remaining energy levels of the sensor nodes; however, these methods often lack adaptability to dynamic network conditions. Specifically, they rely on fixed energy request thresholds, replenish a fixed amount of energy for each node, and fail to account for the density of low-energy sensor nodes. Thus, this paper presents an adaptive framework with three modules for planning the routes of a mobile charger and recharging sensor nodes. (1) An adaptive request threshold is determined by considering the remaining energy of the sensor nodes, the number of energy recharges applied to the sensor nodes, and the number of energy-requesting sensor nodes. (2) A mobile charger path is designed on the basis of networking and node parameters. (3) The amount of energy with which to charge each sensor node is dynamically adjusted instead of depending on conventional approaches that rely on fixed energy recharging amounts. To address network uncertainty issues, our fuzzy logic-based framework is augmented by a genetic algorithm, which uses an encoding scheme where each gene represents a fuzzy rule output. This design enables the genetic algorithm to evolve fuzzy rules and their outputs. In experiments, the proposed approach outperforms the state-of-the-art methods (i.e., first come, first served, nearest-job-next with preemption, and the efficient scheduling scheme) by 14–27% and 4–10% in terms of the network lifetime and the number of sensor nodes with depleted energy, respectively.</p>