In immune invasion detection theory,clonal selection is the key todetectorevolution.The traditional clonal selection algorithm, which compares the cumulative value of affinitybetween samples to select samples, has lower time complexity, but also causes high overlap of detectors and affects the iterative efficiency.This paper transforms the selection and evolution of detector individuals into the solving process of pareto optimal solution, and proposes the detector clone selection algorithm based on multi-objective optimization theory.Experiments show that the algorithm can significantly improve the detection range of each population in the evolutionary process, reduce the number of memory detectors and improve the detection rate of the detection system.