The estimation of distribution algorithm (EDA) is a new type of evolutionary computation approach which reproduces offspring individuals by modeling and sampling the probability distribution of the evolving population. In this paper, the idea of EDA is introduced into the immune multi-objective optimization algorithm to form a hybrid algorithm termed as HIAEDA (hybrid immune algorithm with EDA for multi-objective optimization). It is proposed for solving complex multi-objective optimization problems (MOPs). In HIAEDA, two types of reproducing strategies are combined. One is a recombination and mutation based immune clonal selection operator. It performs a local search around the parent population and develops new searching areas. The other is a EDA based modeling and sampling operator. It learns the variable linkages and promotes the algorithm's capability of solving complex problems. By analyzing the searching behavior of the two operators, the paper comes to the conclusion that their functions are complementary to each other. The convergence of HIAEDA is proved using the theory of the finite Markov chain. Experimental results on benchmarking and real problems show that HIAEDA outperforms the outstanding NSGAII and the EDA based RM-MEDA in terms of both convergence and diversity, especially when solving complex MOPs with nonlinear relationship between decision variables.