E-Mobility is a promising technology for the decarbonization of our society to reach the high set targets to limit the effects of climate change. On the other hand, the increasing penetration of E-mobility leads to grid security issues. The aim of this master thesis is therefore to investigate the effects of market- and grid-oriented charging strategies on the distribution grid for the grid level 7. For this purpose, two different distribution grids will be investigated. A rural grid with few consumers, and a suburban grid with a denser distribution of customers. The investigation will be based on two scenarios, a market-oriented scenario and a grid-oriented scenario. The market-oriented scenario has the goal to minimize the total cost at the grid connection point, without considering the effects on the grid during optimization. In contrast, the grid-oriented scenario is focused on optimizing the effects on the grid, but the costs incurred during optimization have no influence on the result. In a first step the base scenario in both grids is analyzed for the years 2030 and 2040 and the worst-case week will be selected for each grid. The worst-case week is chosen by investigating the node-voltages in each grid. After selecting the worst-case week, the market- and grid-oriented optimization is carried out. For this purpose, a linear optimization algorithm is developed in Python and the additional Pyomo module that can be used for both scenarios by using the weighted sum optimization method. Based on the availability of the car, together with time-series of the residential load and the photovoltaic generation, the optimization is performed. The market-oriented optimization is based on the day-ahead price, while the grid-oriented optimization is based on the total grid load at the transformer. In addition, an iterative approach is used for the grid-oriented optimization, in which the total power at the transformer is updated after each optimization of a household to consider the mutual influence of the consumers. The optimization result is represented by a time-series with the charging load for each consumer. Afterwards, a load flow calculation is carried out using these time-series, in one-minute resolution. The results of the load flow calculation represent time-series for node-voltages and line-utilizations. Furthermore, the energy costs are calculated for each scenario based on the day-ahead price. The market-oriented scenario results in a cost reduction of up to 9.7% and a better grid situation compared with the base scenario in the investigated distribution grids. The better grid situation is shown by reduced maximum line utilization of up to 150% and lower voltage drops of up to +20V at the nodes in the year 2040. This is due to the favorable combination of cheap day-ahead prices during the night hours and the simultaneously low total grid load of non-flexible loads. This results in charging at night instead of in the evening hours. The grid-oriented scenario improves the grid situation by reducing the maximum line utilizations by up to 250% and lower voltage drops of up to +90 V at the nodes for the year 2040 in the investigated distribution grids. This results in less extreme values for line utilizations and node-voltages, which now show a more constant band. In addition, the average energy costs for a consumer with an electric vehicle are reduced by up to 6.3%, which is a result of the favorable combination of cheap electricity during the night and the simultaneously low total grid load of non-flexible loads.