This paper presents an optimal power flow management (OPFM) optimization approach for managing active and reactive energy in a low-voltage microgrid (MG) connected to the main grid that incorporates photovoltaic (PV) systems, battery storage (ESS), a gas turbine (GT), and residential loads. The proposed method employs genetic algorithms to solve the optimization problem, addressing active and reactive power scheduling for distributed sources over a 24-h period. Each source contributes to either active or reactive energy mixes; the goal is to respect the operational constraints of each energy source while aiming to (i) minimize greenhouse gas (GHG) emissions and (ii) reduce expenses. The management approach integrates weather and tariff forecasts to improve decision-making. Results demonstrate the effectiveness of the proposed approach in reducing emissions and maintaining grid stability. Simulations are performed in the MATLAB testing environment to assess the accuracy of the suggested optimization methodology.