Requirements optimization has been widely studied in the SBSE literature. However, previous approaches have not handled requirements interactions, such as the dependencies that may exist between requirements, and, or , precedence , cost and value based constraints. To introduce and evaluate a Multi-Objective Search Based Requirements Selection technique, using chromosome repair and to evaluate it on both synthetic and real world data sets, in order to assess its effectiveness and scalability. The paper extends and improves upon our previous conference paper on Requirements Interaction Management. The popular Multi-Objective Evolutionary Algorithm NSGA-II was used to produce baseline data for each data set in order to determine how many solutions on the Pareto front fail to meet five different requirement interaction constraints. The results for this baseline data are compared to those obtained using the Archive based approach previously studied and the repair based approach introduced in this paper. The repair based approach was found to produce more points on the Pareto front and a better spread of results than the previously studied Archive-based approach. The repair based approach was also found to scale almost as well as the previous approach. There is evidence to indicate that the repair based algorithm introduced in this paper is a suitable technique for extending previous work on Requirements Optimization to handle the requirement interaction constraints inherent in requirement interactions arising from dependencies, and, or , precedence , cost and value based constraints.