The electrification of the heavy-duty freight sector requires a robust charging infrastructure that balances operational needs with grid and cost constraints. This thesis develops an integrated modeling framework to simulate charging demand and optimize the placement and configuration of high power charging stations for HDEVs. The first phase involves modeling charging demand by simulating energy depletion across real world truck trips. Using detailed vehicle specifications and regulatory driving limits, State of Charge (SoC) calculations identify when and where trucks are likely to require charging. These simulated charging events reflect realistic operational behavior across Dutch and cross border freight routes. The second phase applies a Mixed-Integer Linear Programming (MILP) model to determine optimal station locations and configurations. The optimization selects candi- date stations, either generated trough regulatory rules (AFIR) or traffic clustering, and minimizes system wide costs while accounting for wait times, charger availability, and grid connection limits. Events are assigned to specific stations and time slots to ensure feasible, cost effective infrastructure deployment. Two station placements strategies are evaluated for the year 2025 and 2030: (1) AFIR based regulatory , and (2) demand driven locations based on clustering from simulated charging events. The simulated electrification rates are 0.75% for 2025 and 7.5% for 2030. The optimization model provides the number of chargers, where to place them, and how charging events are assigned. From these results, demand curves at the station are created. The findings show that demand based placement can reduce wait times and overall installation cost, while better handling growing charging needs compared to AFIR compliant layouts. Note that the AFIR layout station locations are fixed to the regulatory minimum requirements, and only the number of chargers is adjusted to meet future demand. This thesis presents a practical method for planning charging networks using real truck data. It shows how simulated charging demand can help design better infrastructure, taking into account operational limits and policy goals to support the shift to electric freight transport in Europe.