This article studies the application of multiobjective evolutionary algorithms for solving the energy-aware scheduling problem of workflows in a distributed system that is composed by a federation of datacenters. Nowadays, energy efficiency is a major concern when using large distributed computing systems, including novel grid and cloud computing facilities. Researchers and system planners are looking for accurate methods to be used for planning the execution of large workloads that consume large amounts of resources, having a direct implications for the energy consumption of the system and its operational costs. In the approach proposed in this article, we study the application of multiobjective evolutionary algorithms combined with low-level backfilling heuristics for finding efficient mappings of workflows into resources in order to maximize several metrics related to the quality of service, while reducing the energy required for computation. The experimental evaluation is performed considering both medium and large workloads that model realistic high-performance computing applications and modern distributed computing infrastructures. The experimental results demonstrate that the proposed multiobjective evolutionary approaches compute accurate schedules, significantly outperforming both traditional round-robin/load-balancing schedulers and a set of combined list scheduling heuristics (accounting for both problem objectives) previously applied to the problem.