Reducing energy consumption is an increasingly important issue in cloud computing, more specifically when dealing with High Performance Computing (HPC). Minimizing energy consumption can significantly reduce the amount of energy bills and then increases the provider's profit. In addition, the reduction of energy decreases greenhouse gas emissions. Therefore, many researches are carried out to develop new methods in order to consume less energy. In this paper, we present a multi-objective genetic algorithm (MO-GA) that optimizes the energy consumption, CO2 emissions and the generated profit of a geographically distributed cloud computing infrastructure. We also propose a heuristic that aims to maximize the number of scheduled applications in order to compare it with the MO-GA. The two approaches have been experimented using realistic workload traces from Feitelson's Parallel Workload Archive (PWA). The results show that MO-GA outperforms the heuristic by a significant margin in terms of energy consumption and CO2 emissions. In addition, MO-GA is also proved to be slightly better in terms of profit while scheduling more applications. We also propose in the perspectives how to integrate our approach in the project StratusLab for the exploitation of the geographical dispersion offered by EGI.