Aiming at the common uncertainty in the diagnosis and testing of practical complex systems, an optimal selection method of test points based on artificial immune clone selection algorithm (AICS) is proposed under the unreliable condition. In this model, a fitness function reflecting the performance of the test points is constructed by comprehensively considering the performance indexes such as fault detection rate, isolation rate, false alarm rate and total test cost, and an unreliable test point optimization scheme is designed based on AICS, which effectively reduces the complexity computing. As a result, the time cost is reduced to 0496 seconds, which demonstrates the improvement efficiency of proposed model. Finally, this model is verified by a test utilized with the consumption component in the fuel consumption measurement system. The results show that this method can obtain a set of test points with the lowest test cost while meeting the performance requirements of fault detection rate, isolation rate, false alarm rate, and its comprehensive performance index is better than that of genetic algorithm and simulated annealing particle swarm optimization algorithm.