This paper presents an adaptive control strategy to optimize the sailing maneuvers of an AC75 foiling sailboat competing in America's Cup. Foiling yachts have nonlinear, high-dimensional, and unstable dynamics due to several articulations for fast motions and maneuverability. Achieving aggressive and optimal maneuvers requires taking these complex dynamics into account instead of analytical optimizations using reduced-order models. We compared extremum-seeking and Jacobian learning (JL) control approaches on a full-order model to achieve optimal maneuvers and used JL to optimize articulations. The controllers were integrated with a high-fidelity sailboat simulator for safe and efficient maneuver optimization. The optimal solutions were subject to physical/actuator constraints and those enforced to ensure the feasibility of the maneuvers by humans (sailors). The close-hauled and tacking maneuvers were optimized to achieve maximum velocity made good (VMG) and minimum loss of VMG, respectively. The optimal maneuvers boast a marginal VMG loss of less than 1.5%, which enables exploiting areas of good wind conditions in the racing environment.