The integration of wireless power transfer (WPT) provides a promising solution to address energy limitations in IoT, 5G, and 6G applications. Despite extensive efforts, distributing multiple wireless charging vehicles (WCVs) and scheduling their charging for efficient energy replenishment in wireless rechargeable sensor networks (WRSNs) remains a considerable challenge, leaving a gap in achieving optimal solutions. This article addresses these challenges by formulating the charging scheduling problem in WRSNs, which utilizes multiple WCVs as a multicriteria decision-making (MCDM) problem. We propose a solution that involves two primary steps: 1) a dynamic clustering algorithm is used to partition the deployment area into subareas. After network initialization, the base station (BS) collects sensor nodes in a charging queue. Then, a computation occurs to calculate the total energy required for all sensor nodes in the queue. After the computation, the BS determines the number of clusters based on the available WCVs; and 2) each cluster utilizes an MCDM approach through a fuzzy inference system (FIS) to prioritize nodes for recharging based on multiple network attributes, including remaining energy (RE), distance to the WCV, consumption rate (CR), node density (ND), and time request (TR). The FIS helps identify the sensor node that most requires charging. Our simulation shows that our method outperforms the state-of-the-art techniques in increasing the survival rate (SR), reducing the number of dead nodes, and enhancing the energy utilization efficiency.