Abstract: The dynamic online scheduling method is used in this study to examine the job-shop under dynamic environment. Priority scheduling rules are applied to solve the scheduling cases. For the seven dispatching goals, the best performing scheduling rule is chosen from the alternative scheduling rules set for each goal. An immune scheduling algorithm based on idiotypic network theory is designed in order to choose the scheduling rules dynamically and enable them to adapt to multiple targets. Then, the dynamic control of scheduling rules is realized according to the algorithm and the defined antibody structural model and antigen structural model, as well as through the affinity calculations among these antibodies, antibody concentration calculation, and antibody selection. The simulation results show that immune scheduling algorithm can identify the best dispatching rules quickly to respond to the job-shop situations and meet multiple targets. Thus, the algorithm can effectively deal with the multi-target scheduling problem in the job-shop.