Path planning is able to effectively improve the survival probability and operational efficiency of a combat aircraft. The essence of path planning of the aircraft is a multiobjective optimization problem. To deal with this problem efficiently, this paper proposes an adaptive multiobjective estimation of distribution algorithm named as AMEDA. In AMEDA, a novel clustering-based multivariate Gaussian sampling strategy is designed. At each generation, a clustering analysis approach is utilized to discover the distribution structure of the population. Based on the distribution information, with a certain probability, a local or a global multivariate Gaussian model (MGM) is built for each solution to sample a new solution. A covariance sharing strategy is designed in AMEDA to reduce the complexity of building MGMs, and an adaptive update strategy of the probability that controls the contributions of the two types of MGMs is developed to dynamically balance exploration and exploitation. Experiments show that AMEDA is efficient to deal with the path planning model of the aircraft. Meanwhile, it is convenient to provide multiple flight paths with different characteristics for the decision makers.