Some electric vehicle (EV) owners still worry about having enough juice for extended road trips, even though EVs have a short range and charging facilities are hard to come by. Therefore, making sure the selected path is doable and finding the best way to charge are of utmost importance. This study presents a solution to the EVCSP issue using linear programming with mixed integers (MILP).  By using a piecewise linear approximation technique, this solution tackles the difficulties caused by nonlinear charging times. In order to aid drivers of electric vehicles in making decisions, this method is crucial. For a given route, the proposed optimisation model (CSPM) is in charge of finding the best places to charge an electric car, at the best times, with the most amount of juice to save money and time. Case studies on Turkey and the amount of time it takes to resolve large-scale test problems demonstrate the CSPM's reliability and robustness. The case study is also evaluated after using two types of two techniques for optimising many objectives at once: lexicography and weighted sum. There is a wide variation of 46.09 percent across the various billing approaches that were used, suggesting that the trip cost is highly dependent on the methodology that was selected. However, trip duration is more stable, with a maximum fluctuation of 19.77 percent. Research comparing the CSPM to a full charging method found that it reduced cut expenses by 105.72 percent and saved travel time by 60.01 percent.  This was made possible by the CSPM