In a rush to move towards a sustainable future, the number of electric vehicles has risen significantly in recent years. With this, the need for power to charge those vehicles has also increased. In any electric vehicle fleet location, there could be many vehicles with different arrival and departure times and energy requirements, which might vary every day. Depending on the geographical location, the available solar energy might differ. The electricity costs might change on an hourly basis. This in total can affect the charging costs. In addition, a non-optimal sizing of the energy components could result in an under-sized system, where the energy demands are not met, or it could result in an over-sized system, where the owner must invest more than required. Based on all the information related to vehicle charging load, electricity charges, energy intensity profile of renewable energy generation like solar and wind, an optimal size of components, operational cost, and investment required to operate the station can be determined by various optimization methods. The paper is a comprehensive study of a method of optimization that would result in benefitting the fleet managers to operate their vehicles with minimum costs considering load management, renewable energy investment as well as predictively calculating the energy demand of the fleet. This would then help developers, engineers, and OEMs to calculate the required investments to set up charging infrastructure for fleets.