Violating the safety of autonomous driving systems (ADSs) could lead to fatal accidents. ADSs are complex software-intensive systems, which constantly evolve, i.e., new versions are released one after another. Testing individual ADS is challenging and expensive on its own, and consequently testing multiple versions becomes much more costly. Thus, there is a need to develop test scenario selection and prioritization approaches for testing newer versions of ADSs based on historical test execution data of their previous versions. Motivated by this, we propose a multi-objective search-based approach to select and prioritize test scenarios to test a new version of ADS based on four optimization objectives. We experimented with five commonly used multi-objective search algorithms and used a repository of 60,000 test scenarios. Among all these elected algorithms, IBEA achieved the best performance for solving all the optimization problems of various complexity.