This study proposes an improved clonal selection algorithm based on directed mutation strategy (DMSCSA) to address the problems of the clonal selection algorithm (CSA), such as slow search speed, low convergence accuracy, and easy fall into local optimum. The algorithm introduces the Halton sequence to initialize the population, which enhances the uniformity of the initial population distribution and realizes a more efficient search of the solution space. The golden sine mutation strategy is adopted to conduct the directional mutation of the excellent antibodies in the iterative process, which improves the convergence speed of the algorithm. The introduction of the Cauchy mutation strategy can improve the algorithm’s capability to jump out of the local optimum while ensuring population diversity. Eight different test functions in the CEC2019 test function set are utilized and compared with other algorithms of the same type. The experimental results show that the DMSCSA improves the optimization accuracy and convergence speed.