The command and control information system (command and control system) runs in a dynamically changing and complex environment with constantly changed mission requirements. A self-adaptation decision-making method is urgently needed to dynamically generate the optimal strategy for adjusting the system, so as to adapt to changes in the environment or missions and ensure the long-term stable operation. At present, as the command and control system itself and its operating environment continue to become more complex, self-adaptation decision-making methods need to have the online trade-off decision-making ability to deal with multiple unexpected changes, so as to avoid conflicting adjustment consequences or failure to respond to unknown situations in a timely manner. Nevertheless, the current command and control system mostly adopts self-adaptation decision-making methods based on prior knowledge and responding to single changes, which cannot fully meet this capability requirement. Therefore, this study proposes a self-adaptation decision-making method for the command and control system based on parallel search optimization. This method uses search-based software engineering ideas to model the self-adaptation decision-making problem as a search optimization problem, and uses the genetic particle swarm algorithm to achieve the goal of online weighing against multiple changes that occur at the same time. In addition, in order to solve the problems of search efficiency guarantee and strategy selection in the actual application of this method in the command and control system, this study uses parallel genetic algorithm and POST-optimization theory to parallelize the self-adaptation decision-making method and establish a strategy multi-index sorting method to ensure the practicality of the method.