The difficulty of current multi-objective optimization community lies in the large number of objectives. Lacking enough selection pressure toward the Pareto front, classical algorithms are greatly restrained. In this paper, preference rank immune memory clone selection algorithm (PISA) is proposed to solve the problem of multi-objective optimization with a large number of objectives. The nondominated antibodies are proportionally cloned by their preference ranks, which are defined by their preference information. It is beneficial to increase selection pressure and speed up convergence to the true Pareto-optimal front. Solutions used to approximate the Pareto front can be reduced by preference information. Because only nondominated antibodies are selected to operate, the time complexity of the algorithm can be reduced. Besides, an immune memory population is kept to preserve the nondominated antibodies and ε dominance is employed to maintain the diversity of the immune memory population. Tested in several multi-objective problems with 2 objectives and DTLZ2 and DTLZ3 as high as 8 objectives, PISA is experimentally effective.