The transport sector is a significant driver of climate change and is responsible for a substantial proportion of global CO2 emissions. Battery electric vehicles offer a promising solution for reducing emissions, but require a well-developed charging network for widespread acceptance. The Deterministic Flow Refueling Location Problem (DFRLP) deals with optimizing the placement of charging stations considering traffic flows in order to maximize the coverage of charging demand. This thesis addresses the combination of two existing extensions of the DFRLP. These consider the sizing of charging stations with limited capacity, as well as the cost heterogeneity in urban, suburban and rural areas. A problem-specific decomposition method is developed and applied to efficiently solve this extended DFRLP. The developed decomposition method decomposes a given graph by removing the edges with the smallest traffic volume until the graph is decomposed into smaller clusters to which the extended DFRLP can be efficiently applied. The effectiveness of the decomposition method is demonstrated through extensive numerical experiments. The results show that the solution quality is close to the optimal solution of the full data sets with a significant reduction in runtime. This work contributes to the optimization of electric vehicle charging infrastructure and provides a practical tool for decision making in the field of transportation planning. The proposed method can assist decision makers, infrastructure planners and private investors in optimizing the placement and sizing of charging stations to enable a sustainable transportation future.