In order to increase the interception performance of frequency hopping systems, an optimal algorithm for hopping cycle and hopping interval, which is based on the conditional maximum entropy is proposed. The prior data of frequency hopping systems are used as the training sample space, and the Lagrange multipliers are selected as optimized variables. The Hybrid Chaotic Particle Swarm Optimization (HCPSO) algorithm is used for the optimization of the dual programming of the conditional maximum entropy. Compared with the Single Threshold Method (STM) and the Double Threshold Method (DTM), the simulation results show that the proposed Maximum Entropy Method (MEM) not only has the greatest uncertainty of hopping cycle and hopping interval, it also has the lowest probability of intercept and higher environmental differentiation with threat factors. So the MEM has good RF stealth performance and it can effectively improve the survival ability of the platform.