Despite all the acknowledged advantages and recent developments in terms of reducing the environmental impact, noise reduction and energy efficiency, the electric mobility market is still below the expectations. Among the most important challenges that limit the market penetration of Electric Vehicles (EVs) as well as achieving a sustainable mobility system in cities is the efficient distribution of adequate EV charging stations (CSs). In this paper, we propose a novel approach to find the best locations for EVCSs that considers a combination of factors including displacement between the EV and CS, elevation difference between their locations and finite capacities of CSs. The problem is formulated as a Mixed Integer Linear problem (MILP) to minimize the total energy consumption of EVs to reach CSs. A combination of the Genetic Algorithm (GA) technique and the Branch and Bound (B&B) algorithm are used to solve the problem. The proposed EVCSs placement technique is experimentally tested considering different case studies. With real world datasets, the results demonstrate the energy centric benefits of the proposed EVCSs placement technique.