Contemporary software development is distributed and characterized by high dynamics with continuous and frequent changes to fix defects, add new user requirements or adapt to other environmental changes. To manage such changes and ensure software quality, modern code review is broadly adopted as a common and effective practice. Yet several open-source as well as commercial software projects have adopted peer code review as a crucial practice to ensure the quality of their software products using modern tool-based code review. Nevertheless, the selection of peer reviewers is still merely a manual and hard task especially with the growing size of distributed development teams. Indeed, it has been proven that inappropriate peer reviewers selection can consume more time and effort from both developers and reviewers and increase the development costs and time to market. To address this problem, we introduce a multi-objective search-based approach, named WhoReview, to find the optimal set of peer reviewers for code changes. We use the Indicator-Based Evolutionary Algorithm(IBEA) to find the best set of code reviewers that are (1) most experienced with the code change to be reviewed, while (2) considering their current workload, i.e., the number of open code reviews they are working on. We conduct an empirical study on 4 long-lived open source software projects to evaluate our approach. The obtained results show that WhoReview outperforms state-of-the-art approach by an average precision of 68% and recall of 77%. Moreover, we deployed our approach in an industrial context and evaluated it qualitatively from developers perspective. Results show the effectiveness of our approach with a high acceptance ratio in identifying relevant reviewers.