Nowadays, a mobile object groups management is a relevant scientific problem. The structural complexity, heterogeneity, and geographical distribution, along with the complex criteria of their efficiency estimation make the efficiency improvement of such systems a computationally hard optimization problem. This makes it inexpedient to solve such computational problems by means of analytical or mathematical programming methods usage. Besides, mobile object systems are dynamic in terms of constraints and criteria of their models. So, the question of the resources, which are needed for such computationally hard problem solving, emerges. The aim of this research is to reduce the time required to solve discrete optimization problems in a distributed heterogeneous computing environment while maintaining the solution accuracy level. The novelty of the method proposed in this paper is the technique of distributed optimization problem solving for dynamic computing environments, which is implemented by means of efficient distribution of metaheuristics instances through the available nodes.