The power system stabilizer (PSS) is a major means for suppressing low-frequency oscillation of a power system. This paper proposes a Selected Opposition-Based Grey Wolf Optimizer (SOGWO) for PSS parameter optimization. First, the typical PSS implementation type is selected and the objective function is set in the optimization process. Secondly, a selected opposition-based learning algorithm is used to accelerate the exploration speed and enhance the global search performance of the Grey Wolf Optimizer (GWO). Finally, the validity of the proposed method is verified by employing the IEEE four-machine two-area system model. In addition, PSO, GWO and SOGWO optimize PSS parameters for 100 times each. According to the data which includes maximum, minimum, mean and standard deviation of damping ratio, it can be seen that all three optimization algorithms can avoid falling into local optima and converge quickly, while SOGWO optimizes PSS parameters with better robustness. This work is supported by National Natural Science Foundation of China (No. 51977039).