Autonomous agents offer a promising approach to managing complex, resource-intensive operations such as vehicle routing, scheduling, and offshore decommissioning, where fleets must coordinate interdependent tasks across geographical locations under environmental and logistical constraints. This paper presents a simulation–optimization framework in which autonomous agents make local task selections using meta-heuristic-weighted decision criteria. These criteria, along with schedules and task queue sizes, are co-evolved by a Genetic Algorithm to minimize KPIs. We applied our approach to a North Sea decommissioning case study involving pipeline and structure removal, achieving up to 36% CO2 and fuel usage reduction when compared to a single-vessel baseline. Results show fixed-policy scenarios performed worse, while dynamic configurations enabled more adaptive and efficient scheduling. These findings demonstrate the potential of agent-based optimization to support low-emission campaign planning, aligning with North Sea Transition Authority 50% emission reduction targets for decommissioning activities.