With the advancement of dynamic wireless charging for Electric Vehicles (EVs), Mobile Energy Disseminator (MED), which can charge an EV in motion, becomes available. However, existing wireless charging scheduling methods for wireless sensors, which are the most related works to MED deployment, are not directly applicable for city-scale EV-to-EV dynamic wireless charging. We present MobiCharger: a Mobile wireless Charger guidance system that determines the number of serving MEDs, and their optimal routes. We studied a metropolitan-scale vehicle mobility dataset, and found: most vehicles have routines, and the number of driving EVs changes over time, which means MED deployment should adaptively change as well. We combine EVs' current trajectories and routines to estimate EV density and the cruising graph for MED coverage. Then, we develop an offline MED deployment method that utilizes multi-objective optimization to determine the number of serving MEDs and the driving route of each MED, and an online method that utilizes Reinforcement Learning to adjust the MED deployment when the real-time vehicle traffic changes. Our trace-driven experiments show that compared with previous methods, MobiCharger increases the medium State-of-Charge of all EVs by 50% during all time slots, and the number of charges of EVs by almost 100%.