We describe an approach with considerations for the limitations of operating underwater, including low bandwidth communications, for performing UUV collaborative missions. We implement a decentralized model predictive control (DMPC) algorithm to control teams of unmanned underwater vehicles (UUVs) that simultaneously optimizes vehicle control inputs to explicitly account for the limitations of operating in an underwater environment. We formulate these challenges and limitations, such as vehicle to vehicle communications and collision avoidance, as sub-objectives that are directly including in the optimization problem rather than treating them as constraints. This allows the vehicles to dynamically prioritize the sub-objectives in situ and ensures that a solution to the optimization problem. Additionally, we breakdown the mission into collaborative tasks that allow for a dynamic mission in cases of unplanned circumstances, such as a lost UUV. We demonstrate this scheme in a simulation of a mine counter measure (MCM) scenario in which a heterogeneous mix of UUVs collaborate to detect, locate, and report mine like objects.