Biclustering is a well-known data mining problem in the field of gene expression data. It consists in extracting genes that behave similarly under some experimental conditions. As the Biclustering problem is NP-Complete in most of its variants, many heuristics and metaheuristics are defined to solve for it. Classical algorithms allow the extraction of some biclusters in reasonable time, however most of them remain time consuming. In this work, we propose a new hybrid multi-objective meta-heuristic H-MOBI based on NSGA-II (Non-dominated Sorting Genetic Algorithm II), CC (Cheng and Church) heuristic and a multi-objective local search PLS-1 (Pareto Local Search 1). Experimental results on real data sets show that our approach can find significant biclusters of high quality.