A stochastic approximation expectation maximum (EM) algorithm is proposed to obtain Maximum likelihood (ML) estimation of nonlinear random effect models in which the manifest variables are distributed as a reproductive dispersion model (RDM) and may be missing with ignorable missingness mechanism in this paper. A method composed of simulation step as well as stochastic approximation step is used to obtain the conditional expectation, whereas the M-step is executed via the method of conditional maximization. The most attractive point of this approach is that it is novel and non-trivial, which can be used to obtain the ML estimates and the estimation of standard errors simultaneously. Moreover, A model selection criterion is developed. Empirical results are used to illustrate the usefulness of the methodologies proposed above.