Summary: For the task sets in the fault tolerant real time systems, the disadvantages of the local optimal checkpoint interval are under a single fault assumption and also not the global optimal checkpoint interval. To solve these, the multi-objective optimization model for the checkpoint interval global optimization is given first, and then the checkpoint interval global optimization algorithm based on the mixed particle swarm optimization algorithm is proposed. This algorithm avoids the shortcoming of falling into local optimum and enhances the ability of searching the global approximate optimal checkpoint interval by the crossover and mutation operations of the mixed particle swarm optimization algorithm, and further reduces the task worst case response time. The simulation results show that the algorithm can further improve the system fault resilience over the other checkpoint interval optimization algorithms. At the same time, the checkpoint interval global optimization can search the checkpoint intervals of the task set globally when the faults occur many times, by which the number of checkpoint and the number of fault detection can be reduced and the preemption time by the high priority tasks and the fault recovery time can also be decreased, and also the system schedulability can be improved.