The electrification of urban public transport represents a critical pathway for reducing greenhouse gas emissions and improving air quality in metropolitan areas. This thesis develops and applies a comprehensive optimization framework for the strategic planning of charging infrastructure and battery sizing for electric bus networks, using the city of Ulm, Germany as a case study. A mixed-integer linear programming (MILP) model was developed to simultaneously optimize charging infrastructure placement, battery capacity selection, and charging scheduling strategies while minimizing the net present value of total cost of ownership over a 15-year planning horizon. The model integrates statistical energy consumption based on publicly available GTFS transit data with economic optimization, accounting for battery cost projections, time varying electricity prices, and operational constraints.