Anti-patterns are poor design choices that hinder code evolution, and understandability. Practitioners perform refactoring, that are semantic-preserving-code transformations, to correct anti-patterns and to improve design quality. However, manual refactoring is a consuming task and a heavy burden for developers who have to struggle to complete their coding tasks and maintain the design quality of the system at the same time. For that reason, researchers and practitioners have proposed several approaches to bring automated support to developers, with solutions that ranges from single anti-patterns correction, to multiobjective solutions. The latter approaches attempted to reduce refactoring effort, or to improve semantic similarity between classes and methods in addition to removing anti-patterns. To the best of our knowledge, none of the previous approaches have considered the impact of refactoring on another important aspect of software development, which is the testing effort. In this paper, we propose a novel search-based multiobjective approach for removing five well-known anti-patterns and minimizing testing effort. To assess the effectiveness of our proposed approach, we implement three different multiobjective metaheuristics (NSGA-II, SPEA2, MOCell) and apply them to a benchmark comprised of four open-source systems. Results show that MOCell is the metaheuristic that provides the best performance.