The utilization of stratosphere in earth observing is getting more attention. For maximizing the usage of stratosphere space, it is of vital importance to deploy the platforms of which airship is the representative, properly. Under such a background, a multiobjective model for the deployment problem of multiple airships in the earth observing system (MOM_DP_MAEOS), considering the number of covered tasks and the total profits of the observable tasks, is designed. Furthermore, a multiobjective evolutionary algorithm based on decomposition (MOEA/D) with dynamic weight vectors (DW) and stable matching (STM) schemes (MOEA/D_DW-STM) is proposed to optimize the deployment problem in this paper. DW is applied in the process of updating the individuals in the neighborhoods, playing the role of assisting local search. STM, including a bi-direction selection process, is designed to overcome the shortage that only the process of choosing solutions to subproblems exists in MOEA/D. Besides, K-means clustering operator applied in the initialization and the normalization operator for balancing the search efforts of each objective are applied. A variety of experiments are carried out on different kinds of benchmarks to verify the effectiveness of MOEA/D_DW-STM and the rationality of MOM_DP_MAEOS. Compared with the original MOEA/D and nondominated sorting genetic algorithm II (NSGA-II), the designed algorithm obtains better Pareto fronts (PF) under the same computational cost.