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As electric vehicles (EVs) continue to gain traction, one of the primary challenges remains the limited battery capacity, which restricts the driving range and increases the risk of ”range anxiety.” In this paper, we propose a novel approach to mitigate this issue by leveraging edge computing networks for optimized task offloading. By distributing computational workloads between local EV systems and nearby edge servers or neighboring vehicles, we seek to minimize energy consumption and extend the vehicle’s range. We introduce a hybrid optimization framework that uses a genetic algorithm to efficiently allocate tasks based on both the vehicle’s current energy state and the computational load of nearby resources. Our methodology ensures that energyintensive tasks are offloaded to the most appropriate nodes, thus optimizing the use of available energy and processing power. Experimental results show that this approach can reduce energy usage by up to 30% compared to traditional local processing, offering a promising solution for improving the energy efficiency of electric vehicles in dynamic, real-time environments.