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This article presents a cooperative dynamic task assignment framework for a certain class of autonomous underwater vehicles (AUVs) employed to control the outbreak of Crown-Of-Thorns Starfish (COTS) in Australia’s Great Barrier Reef. The problem of monitoring and controlling the COTS is transcribed into a constrained task assignment problem in which eradicating clusters of COTS, by the injection system of COTSbot AUVs, is considered as a task. A probabilistic map of the operating environment, including seabed terrain, clusters of COTS, and coastlines, is constructed. Then, a novel heuristic algorithm called heuristic fleet cooperation (HFC) is developed to provide a cooperative injection of the COTSbot AUVs to the maximum possible COTS in an assigned mission time. Extensive simulation studies together with quantitative performance analysis are conducted to demonstrate the effectiveness and robustness of the proposed cooperative task assignment algorithm in eradicating the COTS in the Great Barrier Reef. Note to Practitioners—This research is motivated by controlling the outbreak of Crown-Of-Thorns Starfish (COTS), in Australia’s Great Barrier Reef, by employing a certain class of autonomous underwater vehicles (AUVs) equipped with the injection arm. A new evolutionary algorithm, called heuristic fleet cooperation (HFC), is designed upon a hierarchal priority evolution to facilitate a multivehicle dynamic task assignment problem. The HFC is able to rapidly prototype multiple optimal solutions for efficient grouping and accomplishment of the tasks spread over a large operation area. [ABSTRACT FROM AUTHOR]