Phenotype heterogeneity in cancer cell populations, be it of genetic, epigenetic or stochastic origin, has beenidentified as a main source of resistance to drug treatments and a major source of therapeutic failures in cancers. The molecularmechanisms of drug resistance are partly understood at the single cell level (e.g., overexpression of ABC transporters or ofdetoxication enzymes), but poorly predictable in tumours, where they are hypothesised to rely on heterogeneity at the cellpopulation scale, which is thus the right level to describe cancer growth and optimise its control by therapeutic strategies inthe clinic. We review a few results from the biological literature on the subject, and from mathematical models that have beenpublished to predict and control evolution towards drug resistance in cancer cell populations.We propose, based on the latter, optimisation strategies of combined treatments to limit emergence of drug resistance tocytotoxic drugs in cancer cell populations, in the monoclonal situation, which limited as it is still retains consistent featuresof cell population heterogeneity. The polyclonal situation, that may be understood as “bet hedging” of the tumour, thusprotecting itself from different sources of drug insults, may lie beyond such strategies and will need further developments. Inthe monoclonal situation, we have designed an optimised therapeutic strategy relying on a scheduled combination of cytotoxicand cytostatic treatments that can be adapted to different situations of cancer treatments.Finally, we review arguments for biological theoretical frameworks proposed at different time and development scales, theso-called atavistic model (diachronic view relying on Darwinian genotype selection in the coursof billions of years) and theWaddington-like epigenetic landscape endowed with evolutionary quasi-potential (synchronic view relying on Lamarckianphenotype instruction of a given genome by reversible mechanisms), to represent evolution towards heterogeneity, possiblypolyclonal, in cancer cell populations and propose innovative directions for therapeutic strategies based on such frameworks.