Modeling and simulating mine countermeasures (MCM) search missions performed by autonomous vehicles is a challenging endeavor. The goal of these simulations typically consists of calculating trajectories of autonomous vehicles in a designated zone such that the coverage of the zone is above a certain threshold. Starting from existing work in the literature, we implemented the MCM search mission formulation in a stochastic optimal control framework, such that the total mission time needed to survey a designated zone is minimized for a given probability of non-detection of the mines. Our main contribution to the formulation consists of a novel way to model sand ripples, i.e., sand dunes which are present on the ocean floor. The presence of ripples in a designated zone impacts the sea mine detection capabilities of the autonomous vehicles. In order for the autonomous vehicles to be able to detect sea mines in the presence of sand ripples, its trajectories need to be perpendicular to the position of these ripples. Our approach to model them thus consists of multiplying the gamma function, which encloses the sensor model of the autonomous vehicle, with a set of functions containing the soft rectangular function. This approach will force the optimization software to select trajectories that are perpendicular to the position of the sand ripples. We successfully calculated trajectories for up to two autonomous vehicles in a zone consisting of a square domain where sand ripples are present in the left upper triangle.