We present an algorithm to approximately solve certain stochastic nonlinear programs through sample average approximations. The sample sizes in these approximations are selected by approximately solving optimal control problems defined on a discrete-time dynamic system. The optimal-control problem seeks to minimize the computational effort required to reach a near-optimal objective value of the stochastic nonlinear program. Unknown control-problem parameters such as rate of convergence, computational effort per solver iteration, and optimal value of the program are estimated within a receding horizon framework as the algorithm progresses. The algorithm is illustrated with single-commodity and multi-commodity network flow models. Measured against the best alternative heuristic policy we consider for selecting sample sizes, the algorithm finds a near-optimal objective value on average up to 17% faster. Further, the optimal-control problem also leads to a 40% reduction in standard deviation of computing times over a set of independent runs of the algorithm on identical problem instances. When we modify the algorithm by selecting a policy heuristically in the first stage (only), we improve computing time, on average, by nearly 47% against the best heuristic policy considered, while reducing the standard deviation across the independent runs by 55%.