Energy demand is a global crisis as climate changes and the population continues to rise. Hence, is it imperative to produce and distribute energy efficiently; this is commonly referred to as the unit commitment problem. Simulation and optimization are separate approaches to this problem that can synchronize with each other to compensate their unique deficiencies such as the uncertainties associated with simulating renewable power generation. In this paper, we propose a new region-based sampling (RBS) algorithm to determine which demand points to consider based on the region’s priority within the community along with a microgrid (MG) optimization model for each scenario. A case study was conducted on a synthetic microgrid to assess the performance of this approach. The results show that energy supplied but not used (overgeneration) was reduced by eighty percent between the first and second replication according to the sampling region selected by the RBS algorithm.