This study introduces an interactive evolutionary algorithm (EA) for optimizing path planning in groundfish surveys. The approach employs interactive reoptimization to iteratively refine plans by adjusting constraints and incorporating new information, addressing the limitations of traditional optimization models and including real-world factors not initially captured. Developed with input from experienced surveyors, the method provides a practical way to refine survey plans at sea, where fast optimization is crucial. We compare the EA’s performance with a mixed integer programming model applied to a groundfish survey in Iceland involving four vessels, aiming to find the shortest route while considering vessel capacity and port unloading. Results show the EA’s potential in complex marine surveys, highlighting its advantages in interactive optimization and sustainable fisheries management.