Software is playing the most important role in recent industrial innovation, and consequently the amount of software has been rapidly growing last decades. For instance, safety-critical nature of vehicles makes software quality assurance (SQA) has become an essential prerequisite for such innovation. Just-in-time software defect prediction (JIT-SDP) is a special defect prediction method, which aims to conduct software defect prediction (SDP) on commit-level code changes for effective SQA resource allocation. JIT-SDP has advantages of fine granularity, automatic extraction, early application, and traceability. Recent research shows that JIT-SDP prediction model has still rooms for performance improvement since the hyperparameters of the machine learning model are not optimized yet according to characteristics of projects. Search-based software engineering is an approach to solve the problem as search problem formulated by search space and fitness function, e.g., Harmony Search (HS) is a widely used music-inspired meta-heuristic optimization algorithm. In this article, we propose search-based parameter optimization framework on JIT-SDP and demonstrate that our approach can produce the better performance of prediction and reduce effort in practice. Using 8 datasets from both industrial and open source software projects, we obtained an optimized model that meets the performance criterion beyond baseline of previous studies throughout various defect to non-defect class imbalance ratio of datasets. Experiments with open source software also showed better recall for all datasets despite we considered balance as performance index. Search-based parameter optimized JIT-SDP can be applied to the industrial domain software with high class imbalance ratio. We expect that our research can improve the performance of JIT-SDP even in both industrial software and open source software projects with different data characteristics. In addition, the cost-benefit analysis results showed that 20% effort enables the detection of 56% of defects on average and that the post-release quality cost can be reduced by 37.3% in practice. Finally, we also expect that our research can help reduce review effort and post-release quality costs.