Model predictive control(MPC) is an effective method for doubly fed induction generator due to the high dynamic response and multi-objective optimization capacity. However, MPC uses a single cost function to optimize, which leads to the coupling of the control objectives, making it difficult to design the weight factors and control priority. Therefore, a dynamic sequential model predictive control was proposed, which had an optimization structure composed of multiple cascade cost functions. The control objectives were ranked according to the average value of all cost functions of each control objective, and the priority was dynamically adjusted. In addition, the control threshold was used to adjust the number of candidate switch vectors from the control pre-stage to the control post-stage. This method realized the overall optimal control of multiple objectives without using weight factors. The effectiveness of the proposed method was proved by the hardware-in-the-loop results.