The tuning of the weight parameters on the input and output variables can significantly affect the performance of a model predictive controller (MPC) to achieve a good closed-loop dynamic response. However, the currently available approaches based on the bi-layer multi-objective optimization (MOO) for tuning MPC weight parameters are computation-consuming. In this study, a new tuning algorithm is proposed, which converts the bi-layer MOO-based approach into a single-layer nonlinear programming (NLP) problem by treating the sub-optimization problem of MPC in the lower layer as the optimal KKT (Karush-Kuhn-Tucker) condition of the optimization in the upper layer, so as to reduce the computational cost. The simulation results demonstrate that the MPC tuned by NLP method shows similar or even better performance than the MPC tuned by MOO-based method. Moreover, by using the NLP tuning method, the computational time of the MPC tuning can be significantly reduced from a range of 1.0 h to 1.5 h for the MOO-based tuning method to a range of 5 s to 90 s.