The uncertainty of variables such as the intermittency of renewable energy sources (RES), electrical demand alteration, and market price volatility is considered an important issue in the optimization model of microgrids (MG). At present, MG has received growing interest, which needs to administrate the variation related to future changes. The main contribution of this study is to present a stochastic optimization approach for planning the electrical energy of MG considering the intermittency of RES, market price instability, and electrical demand variation. The optimal daily energy scheduling of the MG is required to minimize the pollutant emissions over a 24-h horizon. The general algebraic modeling system (GAMS) is used to solve the optimization problem formulated in this paper. In the suggested stochastic approach, uncertainty modeling of the uncertainty of RESS, electrical demand, and the market price is modeled by the well-known scenario approach, in which the fuzzy C-means (FCM) is employed to cluster the scenarios generated. For the overlapped data set, the FCM gives the best result for it. Thus, FCM operates well than the standard hard clustering algorithm. The results obtained validate the effectiveness of the solution.