During the last years the greenhouse gas emission problems have been extensively discussed and over 70 countries have already committed to a carbon-neutral economy by 2050, seeking for net zero carbon emissions. The electrification of transportation modals has increased following these goals, where Electric Vehicles (EVs) are beginning to take the Internal Combustion Engine Vehicles (ICEV) market share. Besides the problems between EVs and ICEVs comparison, there are challenges involving the nature of EVs and their integration with cities, such as the lack of public locals for charging. The present work aims at studying the problem of a Battery Swapping Station (BSS), a structure where the EVs would only swap their depleted batteries for fully or partially charged ones in a few minutes. In order to simulate the BSS daily decisions, which involves the batteries charging schedule, a Mixed Integer Linear Programming (MILP) problem is developed. Battery heterogeneity and use of photovoltaic (PV) generation was also considered by the model. A case study is presented mixing real and generated data, obtaining results capable of bringing insights for BSS operators, together with charging schedule decisions and BSS sizing plan. The proposed model is coded in Julia language and solved with the support of Gurobi solver. Sensitivity analyses are performed and the CPLEX solver performance is also compared. Possible model extensions are pointed out for future works and the study expects to raise discussions involving the BSS ideal operation.