Despite the restricted range of electric vehicles (EVs) and the limited number of charging stations, there are still drivers who are concerned about whether or not their batteries will be able to survive lengthy road journeys. For this reason, it is of the utmost importance to ascertain the most efficient technique of charging and to make certain that the strategy that is selected is technically possible. The purpose of this study is to demonstrate how the EVCSP issue may be solved by using MILP. This method addresses the challenges that are brought about by nonlinear charging times by using a technique known as piecewise linear approximation. When it comes to supporting drivers of electric cars with decision-making, this technique is very necessary. The suggested optimisation model (CSPM) for a particular route is responsible for determining the most effective sites to charge an electric car at the most ideal times with the most available power. This is done in order to maximise efficiency. The CSPM has been shown to be dependable and robust via the use of case studies on Turkey and the amount of time needed to solve large-scale test difficulties. Prior to the assessment of the case study, the lexicographic approach and the weighted sum method, both of which are multi-objective optimisation approaches, are also used to the case study. It was discovered that the various billing systems had a wide range of 46.09 percent, which indicates that the approach that was selected had a substantial influence on the total cost of the trip. On the other hand, the amount of time spent travelling is very stable, with a maximum variance of 19.77 percent. According to the findings of the study, the CSPM resulted in a cost efficiency improvement of 105.72% and a reduction in travel time equivalent to 60.1% when compared to a full charging strategy. The CSPM was the one that made this a possibility