Conventional direct methods of solving Optimal Control (OC) problems lead to large scale optimization formulations, making the classical optimization solvers more preferable over evolutionary optimization algorithms, for solving the single and multiple objective formulations in OC. On the other hand, population based evolutionary optimization solvers have the ability to identify the global basin efficiently. Therefore, in this paper, a novel method termed as TRANSFORM Artificial Neural Network (ANN) assisted reformulation of OC, has been proposed, which transforms the large scale optimization problem into weight training exercise of auto-tuned ANNs that in turn reduces the scale of optimization by several folds. Through this reformulation, the implementation of evolutionary optimization algorithm is enabled for solving both single and multiple objective OC formulations. Three different benchmark case studies are considered from literature to test the efficiency of proposed algorithm -(a) control of a batch reactor for maximizing the yield of penicillin production, (b) optimal drug scheduling for maximizing the success rates in chemotherapy for cancer treatment, and (c) multi-objective control of plug flow reactor with energy and conversion trade-off. Results indicated an average 50-fold reduction in OC problem size due to ANN reformulation. © 2020 IEEE.