For centuries, the world economics relied on goods distribution and exchange to sustain a sufficient livelihood. Therefore, all transportation means were deployed to surge the frequency of distributions and, accordingly, increase income. However, it was brought to the attention the harmful impacts by vehicles, of all sorts, on the environment due to the important amounts of greenhouse gases they release. To help the environment recover from the global warming caused by these emissions, it was inevitable that internal combustion vehicles be replaced by a more environmentally friendly alternative. This new requirement gave rise to what is known as Electric Vehicle Routing Problem, also known as EVRP, which aims to find the optimal path planning for a fleet of Electric Vehicles to serve a set of customers. In this paper, we propose an Ant Colony Optimization approach to solve the capacitated heterogenous EVRP. The Ant Colony Optimization Algorithm is used to assign a path to each vehicle based on the vehicle’s load capacity. To protect the batteries from degradation, the energy level is kept within a specified range. Upon simulating our model on benchmark dataset’s, our model proved successful in finding an optimal set of paths while minimizing the number of vehicle’s deployed and the overall travelled distance.