Cluster analysis is an important task of data mining, however, traditional biogeography-based optimization algorithm with limitations such as prematurity and poor convergence can not satisfy the demands of solving the NP (Non-deterministic Polynomial) clustering problem. A novel algorithm Mixed Biogeography-Based Optimization (MBBO) is proposed. The algorithm integrates a new migration operator, which is constructed on gradient descent local search, and uses clustering validity index as the individual fitness to optimize implicit cluster structures in datasets. Experimental results on the four benchmark datasets (Iris, Wine, Glass and Diabetes) show that MBBO algorithm outperforms the traditional optimization algorithms such as PSO, BBO, and K-means in terms of clustering validity and convergence, and can acquire the higher quality cluster structures of the datasets.