Abstract—This paper addresses a centralized scheduling problem in multi-agent systems by which the incident commander (I.C.) of a disaster response team aims to coordinate actions of field units (rational agents) in order to minimize the total operation time in uncertain, dynamic, and spatial environments. The purpose of this paper is to propose an intelligent software system that assists the I.C. in dynamic assignment of geospatial-temporal macro tasks to agents under human strategic decisions. This system autonomously executes a heuristic algorithm to minimize the maximum total dependent duration according to human high-level strategies. The result is a schedule composed of macro decisions that each one states seven types of information: 1) what task type is going to be done, 2) who (a subset of agents) are assigned to do this assignment, 3) where (a road segment or zone as a macro geospatial object) contains a subset of tasks, 4) when operations start, 5) when operations finish, 6) how many tasks are estimated to be done, 7) what task types and how many of them are estimated to be revealed in this location after to finish this job. This result, which is a feasible solution for the addressed problem, permits the I.C. to coordinate agents, partially specify activities of agents in time and space, minimize the overall execution time for all tasks, calculate a right time to revise a strategic decision, evaluate the efficiency of a high-level strategy, and estimate the makespan.