Stochastic optimization algorithms such as genetic algorithm (GA) and simulated annealing (SA) are combined with a polynomial-type empirical process model to develop nonlinear model predictive control (NMPC) strategies, namely, GANMPC and SANMPC, in the perspective of control of a nonlinear reactive distillation column. In these strategies, the nonlinear input-output process model is cascaded itself to generate future predictions for the process output based on which the control sequence is computed by stochastic optimizers while satisfying the specified performance criteria. The performance of the proposed controllers is evaluated by applying to single input-single output (SISO) control of an ethyl acetate reactive distillation column with double-feed configuration involving an esterification reaction with azeotropism. The results demonstrate better performance of the stochastic optimization based NMPCs over a conventional proportional-integral (PI) controller, a linear model predictive controller (LMPC), and a NMPC based on sequential quadratic programming (SQP) in tracking the setpoint changes as well as stabilizing the operation in the presence of input disturbances. Although both the GANMPC and SANMPC are found to exhibit almost equal performance, the easier tuning and the lower computational effort suggests the better suitability of SANMPC for the control of a nonlinear reactive distillation column.