Abstract: Biclustering algorithms based on multi-objective optimization, which can optimize several objectives simultaneously in conflict with each other, such as the mean squared residue and the size. In order to mine better biclusters with lower mean squared residue but larger size, a novel algorithm named Multi-objective Artificial Bee Colony Biclustering is proposed. Firstly, the approach adopts a group based representation for the genes-conditions associations to encode foods, then two different crossovers and a mutation operation are used to realize local search and global search respectively. Consequently, the non-dominated sort and crowding distance are applied to prune external archives. Experiments are performed on two real gene expression datasets, and it is found that compared with competing algorithms, the method has better global astringency and diversity of the population. Besides, it can obtain significantly biological biclusters.