Search-based optimization techniques often apply CPU-intensive evolutionary algorithms to explore generations of mutations to a population of candidate solutions. This paper starts with a very large population and sample down to just the better solutions. We call this method “SWAY”, short for “the sampling way”. This paper compares SWAY versus state-of-the-art search-based SE tools using seven models, including linux kernel product line models; and other software process control models. For these models, the experiments of this paper show that SWAY is competitive with corresponding state-of-the-art evolutionary algorithms while requiring orders of magnitude fewer evaluations.