The practical optimal control problems often contain multiple conflicting objectives leading to a set of decision vectors called Pareto Optimal solutions. Population based evolutionary optimizers such as Genetic Algorithms are known to have many advantages over the classical methods when it comes to solving multi-objective optimization problems. However, their applicability in solving large scale optimization problems such as the multi-objective optimal control is severely limited due to the computational time issues. In this paper, we, therefore, present a novel neural network based strategy which reduces the size of optimal control problem (number of decision variables and constraints) by several folds. The reformulation of optimal control problem into a simple weight training exercise allows the implementation of evolutionary solvers to achieve high quality Pareto solutions. We demonstrate our technique for (i) the design of a plug flow reactor with conflicting energy and conversion costs and (ii) the control of a fed batch bioreactor with a conflict between yield and productivity. © 2018 IEEE.