Background and Purpose: There has been a recent recognition that continuous chemotherapy at the maximum tolerated dose (MTD) is just one of infinite possible viable treatment schedules. How to best schedule existing cancer therapies to achieve cure (extinction therapy) or maximize progression-free survival (PFS; palliative therapy) remains an open question. There is a need to systematically enumerate the optimal strategies both for extinction and palliative therapy across all regions of drug-patient-disease parameter space. Methods: Here, we used a previously introduced minimal model of cancer treatment resistance—the GDRS model, capturing tumor (exponential) Growth, cell Death due to drug (log-kill, with no collateral resistance/sensitivity), the development of Resistance to drug, and tumor re-Sensitization to drug—to study the effects of different strategies. We sampled GDRS model space by exhaustively enumerating the possible different combinations of parameter values. When possible, we did a brute-force search for the optimal treatment strategy, simulating each possible schedule and assessing it as an extinction therapy (minimum tumor volume reached) and as a palliative therapy (time until volume doubled). When the number of drugs or treatment cycles increased such that brute-force enumeration was no longer possible, we numerically found the optimum using a simulated annealing algorithm. Results: For treatment with one drug, we were able to construct a phase diagram of the optimal strategies over GDRS parameter space, for both curative intent and maximizing PFS. We found that continuous MTD treatment is the optimal palliative strategy when re-sensitization is very weak compared to the development of resistance. It is also the optimal extinction strategy, except when re-sensitization becomes strong enough that regular treatment holidays become beneficial. At intermediate levels of re-sensitization, adaptive therapy (with treatment holidays whose length depends on tumor volume regrowth) is the optimal palliative strategy. For two drugs, when re-sensitization was strong compared to resistance, rapidly alternating the drugs was the optimal strategy for both extinction therapy and palliative therapy. At very lower re-sensitization values, a “second-strike” strategy of switching at the nadir was the optimal extinction strategy. For palliative therapy at these re-sensitization values, alternating, second-strike, and sequential MTD all performed about the same. Conclusions: By constructing a phase diagram of optimal treatment strategies, we were able to identify heuristics for when various previously proposed evolution-inspired cancer treatment schedules work best. The algorithm developed here can be applied to any patient whose GDRS parameters are known to give their personalized optimal treatment schedule. Such predictions need to be tested experimentally and clinically.