t is increasing the needof improve the existent sources, and find greenerways to generate electrical energy. Besides,there is the necessity of avoid the energy wasting by using the available power in amore efficient way. With a proper data preprocessing, the multi-objective search and optimization algorithms have been used as potential appliance usage recommender techniques in order to energy savings. The proposal of this work is anappliance context recommendation system using a binary multi objective particle swarm optimization technique capable of, based on a given context, recommend new contexts which would spend less power than the current, byminimally affectingthe user’scomfort. The energy savings, for the first analyzed context recommendations, were: S1: 2,05%, S2: 8,27%, and S3: 1%. And for the secondcontext, were: S1: 5,76%, S2: 0,5%, S3: 2,71%, and S4: 4,82%. The developed technique was able to find solutions that spend less power and are more invasive to the user normal behavior,as well assolutions that have less power economywith a minor intervention to the user normal behavior. This technique makes sense in a Smart Home management system which is still being developed.