In this paper, we propose an improved multi-objective immune algorithm based on differential evolution, named DE-MOIA. In recent years, the multi-objective immune algorithm (MOIA) has shown promising performance in solving multi-objective optimization problems. However, existing MOIAs still have difficulties to obtain high quality results. In order to find a set of solutions that closely approximate the Pareto-optimal front (PF), we present DE-MOIA, which uses two differential evolution strategies and an adaptive mutation operator. We performed experiments using eight benchmark multi-objective problems. In order to validate the effectiveness of our algorithm, we compared DE-MOIA with three multi-objective evolutionary algorithms (MOEAs) and a multi-objective immune algorithm (MOIA) on three performance metrics. Experimental results show that our algorithm performs better than other algorithms.