Reducing energy consumption in large-scale computing facilities has become a major concern in recent years. The large number of computing nodes, resources heterogeneity and diversity of application requirements are factors that turn the scheduling into an NP-Hard problem. Evolutionary algorithms have proved to be effective for scheduling applications. In this paper, we present a novel approach combining particle swarm optimization and a genetic algorithm to solving the resource matching and scheduling of parallel applications in Federated cluster environments. The proposed hybrid meta-heuristic, referred to as MPSO-FGA, not only minimizes the overall energy consumption but also the makespan for a whole workload. The experimental results show the superiority of evolutionary algorithms over basic heuristics. The hybrid meta-heuristic is able to obtain similar results to a genetic algorithm in terms of energy consumption and makespan but reducing the time for scheduling decisions by two orders of magnitude.