In agile software development, regression testing is an ongoing process activity. However, real-world time constraints necessitate selecting a subset of tests to run. Current regression test selection algorithms primarily focus on technical metrics such as requirement coverage while overlooking the business value each test validates. This study reframes regression test selection as a multi-objective optimization problem in which tests are selected to maximize business value within a constrained testing time while maintaining adequate requirement coverage. We apply an artificial intelligence-based search algorithm, and our results show that the proposed method consistently selects tests with higher business value than baseline approaches when time is limited, while maintaining comparable requirement coverage. These findings suggest that a driven value-aware selector can be incorporated into agile teams for decisions on allocating limited regression testing resources.