Genetic Improvement (GI) is an area of Search Based Soft- ware Engineering which seeks to improve software’s non- functional properties by treating program code as if it were genetic material which is then evolved to produce more op- timal solutions. Hitherto, the majority of focus has been on optimising program’s execution time which, though im- portant, is only one of many non-functional targets. The growth in mobile computing, cloud computing infrastruc- ture, and ecological concerns are forcing developers to fo- cus on the energy their software consumes. We report on investigations into using GI to automatically find more en- ergy efficient versions of the MiniSAT Boolean satisfiability solver when specialising for three downstream applications. Our results find that GI can successfully be used to reduce energy consumption by up to 25%.