We demonstrate how to learn the objective function and constraints of optimization problems while observing its optimal solution over multiple rounds. Our approach is based on Online Learning techniques and works for linear objective functions under arbitrary feasible sets by generalizing previous work. The two algorithms, one to learn objective function and other to learn constraints, converge at a rate of O (1 on t root) that allow us to produce solutions as good as the optimal in a few observations. Finally, we show the efficacy and possible applications of our methods in a significant computational study.