Modern code review is a common practice used by software developers to ensure high software quality in open source and industrial projects. During code review, developers submit their code changes which should be reviewed, via tool-based code review platforms, before being integrated into the codebase. Then, reviewers provide their feedback to developers, and may request further modifications before finally accepting or rejecting the submitted code changes. However, the identification of appropriate reviewers is still a tedious task as the number of code reviews to be performed is inflated with the increasing number of code changes and the increasing size of software development teams in today’s large and active software projects. To help developers with the review process, we introduce a multi-objective search-based approach to find the appropriate set of reviewers. We use the Non-dominated Sorting Genetic Algorithm (NSGA-II) to optimize two conflicting objectives (i) maximize reviewers expertise with the changed files, and (ii) minimize reviewers workload in terms of their current open code reviews. We conduct a preliminary evaluation on two open source projects to evaluate our approach. Results indicate that our approach is efficient as compared to state-of-the-art approaches.