Multi-objective evolutionary algorithms (MOEAs) have been the mainstream to solve multi-objectives optimization problems In this paper we add the static, Bayesian game strategy into MOGA and propose a novel multi-objective genetic algorithm(SBG-MOGA) Conventional MOGAs use non-dominated sorting methods to push the population to move toward the real Pareto front This approach has a good performance at earlier stages of the evolution, however it becomes hypodynamic at the later stages In SBG-MOGA the objectives to be optimized are similar to players in a static Bayesian game A player is a rational person who has his own sit elegy space A player selects a strategy and takes all action to realize its strategy in order to achieve the maximal income tor the objective he works on The game strategy will generate A tensile force over the population and this will obtain a better multi-objective optimization performance Moreover, the algorithm is verified by several simulation experiments and its performance is tested by different benchmark functions