Reducing energy consumption in large-scale computing facilities has become a major concern in recent years. Most of the techniques have focused on determining the computing requirements based on load predictions and thus turning unnecessary nodes on and off. Nevertheless, once the available resources have been configured, new opportunities arise for reducing energy consumption by providing optimal matching of parallel applications to the available computing nodes. Current research in scheduling has concentrated on not only optimizing the energy consumed by the processors but also optimizing the makespan, i.e., job completion time. The large number of heterogeneous computing nodes and variability of application-tasks are factors that make the scheduling an NP-Hard problem. Our aim in this paper is a multi-objective genetic algorithm based on a weighted blacklist able to generate scheduling decisions that globally optimizes the energy consumption and the makespan.