With recent advances in battery technology and the resulting decrease in the charging times, public charging stations are becoming a viable option for Electric Vehicle (EV) drivers. Concurrently, wide-spread use of location-tracking devices in mobile phones and wearable devices makes it possible to track individual-level human movements to an unprecedented spatial and temporal grain. Motivated by these developments, we propose a novel methodology to perform data-driven optimization of EV charging stations location. We formulate the problem as a discrete optimization problem on a geographical grid, with the objective of covering the entire demand region while minimizing a measure of drivers' discomfort. Since optimally solving the problem is computationally infeasible, we present computationally efficient, near-optimal solutions based on greedy and genetic algorithms. We then apply the proposed methodology to optimize EV charging stations location in the city of Boston, starting from a massive cellular phone data sets covering 1 million users over 4 months. Results show that genetic algorithm based optimization provides the best solutions in terms of drivers' discomfort and the number of charging stations required, which are both reduced about 10 percent as compared to a randomized solution. We further investigate robustness of the proposed data-driven methodology, showing that, building upon well-known regularity of aggregate human mobility patterns, the near-optimal solution computed using single day movements preserves its properties also in later months. When collectively considered, the results presented in this paper clearly indicate the potential of data-driven approaches for optimally locating public charging facilities at the urban scale.