We formulate the Lebesgue-sampling-based optimal control problem. We show that the problem can be solved by the time aggregation approach in Markov decision processes (MDP) theory. Policy-iteration-based and reinforcement-learning-based methods are developed for the optimal policies. Both analytical solutions and sample-path-based algorithms are given. Compared to the periodic-sampling scheme, the Lebesgue sampling scheme improves system performance.