Dynamic is widely encountered in the chemical industry, which may come from equipment aging or catalyst deactivation. Meanwhile, the drift of these variables generally has a certain trend of change, such as worse equipment status or lower catalyst activity. Most of the existing optimization methods focus on steady-state optimization problems, and the optimization schemes proposed for such dynamic optimization problems are generally time-consuming. Therefore, to achieve optimal economic benefits, drifting operating conditions are challenges that must be overcome. In this work, to use the prior knowledge of the research object and guide the algorithm to converge fast, incremental encoding (IE) combined with the population is introduced, which searches for a feasible and better control trajectory. Next, the coarse random search (RS) method often used in iterative dynamic programming is introduced to improve the performance of the algorithm. The proposed two-step IE-RS optimization algorithm based on the control vector parameterization (CVP) combines the advantages of a heuristic algorithm and iterative dynamic optimization, which not only ensures fast convergence but also ensures the effectiveness of the algorithm, and is finally demonstrated in the acetylene hydrogenation process.