In recent years, Shared Mobility-on-Demand systems have emerged as a great method for door-to-door transportation. Studies have shown that it is possible to route vehicles and assign requests to vehicles efficiently in large-scale systems. These studies commonly report one-dimensional performance metrics such as average vehicle occupancy, service rate, or average waiting time. We repeated a case study using a state-of-the-art Fleet Management Framework and focused on the distribution of the service level over the operation area. We observed that the chance of receiving service in a low demand area was much higher than in a high demand area. Going from this observation, this research’s objective was to research how the state-of-the-art framework can be adjusted such that the rejection rates are more evenly spread over the operation area. We developed different methods that adjust the decision of which mobility requests are serviced or which trips are selected. The methods work such that a request located in an above-average rejection rate area has an increased chance of being serviced. Similarly, a trip that goes through an area of above-average rejection rate also has priority. We set up a Discrete Event Simulation that simulates a Shared Mobility-on-Demand system to research the effects of our added method compared to the original framework. We simulated an artificial city and New York City. The Gini index was used to measure how evenly the rejection rates were spread over the operation area. In many cases, our methods were able to lower both the average rejection rate and the Gini index. With this work, we showed that the state-of-the-art framework’s objective can be extended to a broader goal. This opens up new possibilities to tune the system to match specific transportation needs in different areas of a city.