We formulate the optimal dynamic sampling allocation decision problem for feasibility determination as a stochastic control problem in a Bayesian setting. This new formulation addresses the limitations of previous static optimization formulations. In an approximate dynamic programming paradigm, we propose an approximately optimal allocation policy that maximizes a single feature of the value function one step ahead. Numerical results demonstrate the efficiency of the proposed method.