In this chapter, a more general linear stochastic system with intrinsic random fluctuations due to continuous Wiener process and discontinuous Poisson process as well as external disturbance is introduced to model a linear dynamic system under intrinsic random fluctuations and environmental disturbance in engineering, social, and bioscience. We introduce the multi-player noncooperative H_∞ game strategy of linear stochastic jump-diffusion systems at first. Each player wants to design a parsimonious control effort to optimally achieve its desired target tracking with the worst-case effect of competitive strategies and external disturbance on the tracking performance to be attenuated as possible from the minmax H_∞ target tracking perspective. Unlike the conventional methods to iteratively search for the Nash equilibrium solution one player by one plyer, in this chapter, the multi-player noncooperative game problem is transformed to an equivalent multiobjective optimization problem (MOP). The m-player noncooperative game strategy design problem is transformed to a corresponding linear matrix inequalities (LMIs)-constrained MOP, which could be solved efficiently by the proposed LMIs-constrained multiobjective evolutionary algorithm (MOEA). If the linear stochastic system is free of external disturbance, then the m-player noncooperative H₂ game strategy design problem is also introduced from the conventional minmax quadratic target tracking perspective. The m-player noncooperative H₂ game strategy problem of linear stochastic system could also be transformed to a corresponding LMIs-constrained MOP, which could also be efficiently solved by the proposed LMIs-constrained MOEA. Further, the m-player cooperative H∞ and H₂ game strategy design problems of linear stochastic systems could be transformed to the corresponding LMIs-constrained single-objective optimization problems (SOPs), which could be easily solved with the help of LMI-toolbox in Matlab.