The hybridization of population-based meta-heuristics and local search strategies is an effective algorithmic proposal for solving complex continuous optimization problems. Such hybridization becomes much more effective when the local search heuristics are applied in the most promising areas of the solution space. This paper presents a hybrid method based on Clustering Search (CS) to solve continuous optimization problems. The CS divides the search space in clusters, which are composed of solutions generated by a population meta-heuristic, called Variable Mesh Optimization. Each cluster is explored further with local search procedures. Computational results considering a benchmark of multimodal continuous functions are presented.