Energy efficient scheduling and allocation in multicore environments is a well-known NP-hard problem. Nevertheless approximated solutions can be efficiently found by heuristic algorithms, such as evolutionary algorithms (EAs). However, these algorithms have some drawbacks that hinder their applicability: typically they are very slow, and if the space of the feasible solutions is too restricted, they often fail to provide a viable solution. In this paper we propose an approach that overcomes these issues. The approach is based on a custom EA that is fed with predicted information provided by an existing static analysis about the energy consumed by tasks. This solves the time inefficiency problem. In addition, when this algorithm fails to produce a feasible solution, we resort to a modification of the well-known YDS algorithm that we have performed, well adapted to the multicore environment and to the situations when the static power becomes the predominant part. This way, we propose a combined approach that produces an energy efficient scheduling in reasonable time, and always finds a viable solution. The approach has been tested on multicore XMOS chips, but it can easily be adapted to other multicore environments as well. In the tested scenarios the modified YDS can improve the original one up to 20%, while our EA can save 55 − 90% more energy on average than the modified YDS.