The charging of electric vehicles (EVs) at residential premises is orchestrated through either centralized or decentralized control mechanisms. The former emphasizes adherence to power grid constraints, employing demand management techniques to restrict EV charging when the aggregated demand exceeds a predetermined threshold, which may result in user discontentment. Conversely, the latter endows EV users with the authority to self-regulate their charging behavior to optimize cost, allowing a multitude of interconnected EVs to charge during the same off-peak window. However, this decentralized approach gives rise to the herding problem, wherein a simultaneous surge in EV charging during off-peak periods burdens the power grid, leading to potential system overloads. This paper presents a hybrid coordinating scheme that integrates a fuzzy inference mechanism to synergistically blend the merits of centralized and decentralized coordinations. The proposed hybrid coordination scheme aims to minimize peak load, alleviate herding, and optimize charging costs while ensuring adherence to EV users’ charging obligations at the lowest feasible expense. The problem is formulated with the introduction of a novel fuzzy objective function and subsequently resolved through the fuzzy inference mechanism. The fuzzy inference encapsulates independent and uncertain price profiles, consumption load patterns, and state-of-charge data collected from the power grid, households, and EV domains, which are effectively integrated into weighted variables for the requesting EVs. The proposed hybrid coordinating scheme leverages weighted variables to optimize the objective function, enabling the determination of an optimal charging schedule that satisfies the charging requirements of the requesting EVs, while adhering to stringent power grid operational constraints and minimizing charging costs. To assess the efficacy of the hybrid coordination scheme, we conducted two meticulous case studies employing the IEEE 34 bus system as a testbed, thoroughly evaluating performance metrics encompassing charging cost, load profile impact, and peak-to-average ratio. The results demonstrate the superior performance of the proposed hybrid coordination scheme compared to alternative charging strategies, including uncoordinated charging, standard-rate charging, time-of-use charging, and two-layer decentralized approaches.