Abstract: The intermittency and random uncertainty of wind power generation are the main obstacles to large-scale grid connection. In order to improve the ability of wind turbine control systems and deal with wind energy random disturbance, an affine-disturbance-feedback-based multi-objective stochastic model predictive control (ADF-MSMPC) strategy is proposed in this paper. Aiming at accurately tracking the power output set-point and reducing the actuators' fatigue loads, a stochastic optimization model consisting of expected objective function and chance constraints is constructed based on the turbine realistic operating condition and constraints. With the aid of affine disturbance feedback policy and wind speed probability information, the stochastic optimization model is then transformed into a tractable deterministic optimization model, and therefore the current optimal control law can be obtained. The benchmark NREL 5MW wind turbine is chosen as the research object in this paper, and the simulation results show that through selecting the appropriate probability value of output constraint violation, the proposed control strategy can effectively suppress the random wind speed disturbance, reduce the actuators' action amplitudes and realize the accurate tracking of rated power output.