Abstract: Traditional multi objective evolutionary algorithm (MOEAs) may result in slow convergence on Pareto front and poor distribution of solution sets for MOPs. In order to deal with these shortcomings, this paper proposes a multi-objective biogeography optimization algorithm based on the migration model of dominance degree (MOBBO). The proposed migration model makes full use of the information among the Pareto solutions so as to carry out the effective individual evaluation and the habitat sorting. In addition, this paper presents a self-adaptive migration strategy based on feature database for producing offspring with better features to strengthen the search ability. Meanwhile, in order to enhance the distribution of solutions during the evolution, this paper further modify the K-nearest neighbor (KNN) density estimation methods to discard overcrowded individuals. Numerical experiments on ZDT and DTLZ series and the condensation process of MDI verify the fast convergence and good distribution of MOBBO.