Genetic algorithms, owning to their characteristics of intrinsic parallel mechanism and full optimization, can be used to search for solutions for objective programming. In this study, empirical study method was combined with the multi-objective optimization to develop a multi-objective forest harvest adjustment model by introducing Pareto multi-objective genetic algorithm into optimization model. This methodology was applied to Fengshu Mountain tree farm, Jiangxi province and the obtained results showed that integrating of the genetic algorithm with the multi-objective programming of the forest harvest adjustment led to non-inferior solution set which provided much greater choice space for decision-making. Moreover, this method overcame the shortcomings that exist in the original harvest adjustment procedure in which different objective weight values are needed. In addition, the genetic algorithm successfully achieved the adjustment of Masson Pine age-class area and balancing rate as well as increased volume and volume yield per unit area.