A new optimization framework is presented which is able to identify optimal solutions for maximum profit design of subsurface drip irrigation (SDI) systems under limited supply. To solve this complex optimization problem, decomposition is used which splits the problem into three sub-problems: (i) optimal irrigation control, which maximizes water distribution uniformity and minimizes percolation losses; (ii) optimal irrigation scheduling, which minimizes irrigation water applied in order to meet a high yield with a specified reliability; and (iii) optimal drip line layout, which includes the solutions of the other sub-problems and maximizes profitability. The multi-level optimization framework was tested in France with corn cultivated on two SDI plots with drip line spacing of 1.2m (SDI120) and 1.6m (SDI160), respectively. HYDRUS simulations estimated adequate irrigation amounts of 20-35mm per event. For optimal irrigation scheduling, initial schedules were provided at sowing and adapted weekly according to observed weather data and synthetic weather scenarios. The presented framework significantly increased profit and water productivity for deficit SDI designs. The latter was increased up to 30% (SDI120), compared to seven other irrigation experiments. The optimal SDI design was achieved by SDI160, which increased profitability by 27% compared to SDI120.