To address the task scheduling problem in cloud, a new multi-objective immune system algorithm named IMISA (Improved Multi-objective Immune System Algorithm) was proposed to optimize completion time and monetary cost simultaneously. In this work, the assignment of the fitness value was different from the traditional way, the antibodies were divided into non-dominated antibodies and dominated antibodies. The exclusive dominated area was regarded as the antigen affinity, and the polygonal area surrounded by all the non-dominated solutions and a selection of the dominated antibody were treated as the corresponding antibody-antigen affinity. After that, clonal probability was calculated according to these affinities and new offspring was generated by cloning and mutation. Simulation experiments on CloudSim indicate that, compared with NSGA-Ⅱ and Multi-objective Immune System Algorithm (MISA), the proposed algorithm can produce schemes with shorter completion time and lower monetary cost, and it also can achieve Pareto fronts with better quality in both convergence and diversity.