This paper describes a new approach for yield sampling in viticulture. It combines approaches based on auxiliary information and path optimization to offer more consistent sampling strategies, integrating statistical approaches with computer methods. To achieve this, groups of potential sampling points, comparable according to their auxiliary data values are created. Then, an optimal path connecting several points, one from each group of potential sampling points and minimizing the route distance is created. This part is performed using constraint programming, a programming paradigm offering tools to deal efficiently with combinatorial problems. The paper presents the formalization of the problem, as well as the tests performed on real fields. Results show that combining target sampling and path optimization can reduce by 45% the average sampling circuit length compared to previous methods based on auxiliary data while being almost equivalent in yield prediction error.