We present a multiobjective genetic algorithm based on the FDH model, called FDHMOGA. The algorithm evaluates the performance of all the individuals of the population and makes choice according to the efficiency value obtained from evaluation and congestion degree, which can improve the local search ability. Meanwhile, we use the partition strategy to enlarge the search range and improve the diversity of the solutions. We adopt several test functions and portfolio optimization models to compare the performance of the FDHMOGA. The results show that the FDHMOGA algorithm has better computation performance and is more effective.