In recent years, the rise of electric vehicles (EVs) has led to the replacement of traditional internal combustion engine (ICE) vehicles. As a result, EV charging station (EVCS) planning has become an integral part of distribution network planning. Additionally, the increasing use of renewable energy sources in power generation has introduced microgrids into the distribution system. This research paper focuses on the multi-objective planning optimization of a microgrid integrated with EVCS and utilizes renewable energy sources, such as photovoltaic (PV) and wind turbines. The objectives of this optimization are twofold: to minimize the annual cost and to maximize customer satisfaction within the microgrid. To achieve this, the paper considers demand-side management (DSM) and controlled EV charging scheduling using time-of-use charges for EVs. To estimate the EV demand, a fuzzy inference system (FIS) is utilized. Since cost minimization and microgrid consumer satisfaction maximization are conflicting objectives, a fuzzy satisfaction maximizing method is employed to convert the multi-objective optimization problem into a single objective. The two single-objective optimization problems are solved by using a mixed integer linear programming (MILP) algorithm individually. By applying this approach, the paper successfully obtains the optimum values for cost and user satisfaction factors, thereby providing valuable insights for the planning and optimization of microgrids integrated with EVCS and renewable energy sources.