Abstract: A kind of time-varying parameter non measurable dynamic multi-objective optimization genetic algorithm based on reference points is proposed for the design of PID controller with variable parameters and multiple objective functions. The algorithm is a dynamic multi-objective optimization genetic algorithm which joins the reference point and local search and population updating mechanism to optimize the parameters of the PID controller under the conditions of different environment and undetectable changes. In order to verify the effectiveness of the algorithm, a typical test function is used to compare this algorithm with the DNSGA2-A algorithm. The thought of PID controller design is as follows. Firstly, a dynamic multi-objective model of a PID controller is established, and the designing PID controller tuning problem is formulated as a dynamic multi-objective optimization problem. Secondly, reference points are established and then a dominant Pareto dominance relationship based on reference points is defined. In addition, the population is processed through a local search and archive update. And the dynamic multi-objective optimization algorithm is used to optimize the PID parameters. Finally, the method is applied to the optimization problem of the diesel engine. To shorten the error and variance, as the optimization goal, the three parameters of the PID controller are optimized. The dynamic multi-objective optimization evolutionary algorithm for PID controller parameter optimization is validated effectively.