<p>The development of electric vehicles, driven by environmental imperatives, is a rapidly growing field, particularly in the freight sector. However, widespread adoption faces unique challenges, including payload capacity, battery limitations, charging infrastructure, and charging speed. This research introduces a parallel Ant System implemented using CUDA to address the Electric Vehicle Routing Problem with Time Windows (EVRPTW). Comprehensive experimentation was conducted on benchmark datasets, with performance compared against other heuristic approaches such as the NEH algorithm and Genetic Algorithms, leveraging a pairwise seed-based methodology. The results demonstrate significant scalability and adaptability of the proposed algorithm, achieving high-quality solutions efficiently.</p>