In 2016, the Dutch government, in pursuit of the UN’s sustainable development goals, set a target that all its diesel transit bus networks should be fully electrified between 2025 and 2030. A research team from Rotterdam School of Management has since worked in close collaboration with Rotterdamse Elektrische Tram, the public transport operator in the city of Rotterdam, to accomplish this complex transition. This paper presents essential lessons learned and key practical implications derived from the project. As part of the transition process, we developed a discrete-event simulation model that can simulate the network using different settings and under uncertainty. We also formulated a mixed-integer linear programming problem to optimize the charging schedule. To mitigate the critical impact of uncertainty regarding traffic delays and energy consumption on the electrified transit bus network operation, we developed a real-time decision support system that adjusts and reoptimizes the charging schedule during the day according to the realizations of this uncertainty. We use this system to achieve better coordination between the charging schedule of the electric buses and electricity generation from renewable energy sources with the latter involving high levels of uncertainty. Our study shows the benefits of real-time optimization compared with off-line planning and other greedy strategies. We also show that even highly conservative off-line planning might not be sufficient to maintain reliability levels under extreme operational uncertainty conditions. Additionally, our results and insights have substantially contributed to the success of the first phase of the project, which involved electrifying seven essential bus lines in the city, in realizing a robust and reliable operational plan. Finally, our study shows the potential substantial positive impact of installing renewable energy generators and coordinating the electric buses’ charging schedule with their output power profile. Based on our recommendations, RET developed a real-time monitoring system and is working on incorporating our charging schedule optimizer into its planning process.