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Successful stimulation therapies of the central nervous system for chronic neurological disorders have been based so far on electric pulses that have equal amplitude and are delivered at constant intervals. Recent advancements, however, have shown that irregular and time-varying sequences of pulses can be equally effective in treating chronic disease conditions. This suggests that both pulse waveform and temporal arrangement are important factors in determining the therapeutic merit of a stimulation protocol and can be used to address the trade-off between therapeutic effectiveness, amount of charge delivered per unit of time, and efficiency of neural stimulators. Accordingly, a wide range of computational approaches have been developed to optimize this trade-off, and novel nonregular pulse trains have been designed. Optimization, adaptive control, and machine learning have been rapidly integrated into the design process of stimulation therapies, leading to highly efficient solutions but also dramatically increasing the complexity of the design process. This chapter will review the most significant advancements in optimization-based design for neural stimulation, along with the computational challenges, methodological innovations, and the most promising clinical applications for the treatment of the central nervous system.