Many real world optimization problems have multiple objectives that typically are in conflict with one another. Furthermore, at least one objective can even be dynamic. If all of these traits are present, the problem is called a dynamic multi-objective optimisation problems (DMOOPs). The non-dominated sorting genetic algorithm II (NSGA-II) is a standard or benchmark algorithm for static multi-objective optimization problems (MOOPs) that has been extended to solve DMOOPs. Once a change has been detected, an algorithm has to react appropriately, to ensure enough diversity in the population to search for new optimal solutions after the change has occurred. However, the algorithm still has to balance exploration and exploitation. Therefore, this paper investigates four change reaction strategies that introduce new diversity into the population of the dynamic non-dominated sorting genetic algorithm II (DNSGA-II) after a change in the environment has occurred. The results indicate that all strategies that only inject diversity through changing a portion of the population (and not the entire population) performed well. When the whole population was changed, the performance of DNSGA-II deteriorated.