Biclustering or simultaneous clustering of both genes and conditions have generated considerable interest over the past few decades, particularly related to the analysis of high-dimensional gene expression data in information retrieval, knowledge discovery, and data mining. The objective is to find sub-matrices, i.e., maximal subgroups of genes and subgroups of conditions where the genes exhibit highly correlated activities over a range of conditions. Since these two objectives are mutually conflicting, they become suitable candidates for multi-objective modeling. In this study, a novel multi-objective evolutionary biclustering framework is introduced by incorporating local search strategies. The experimental results on benchmark datasets demonstrate better performance as compared to existing algorithms available in literature.