Microalgae is a promising source of renewable biofuels, and optimization and control of the biomass growth stage can make techno-economic improvements. This work explores the development of an algae growth model from first-principles, which includes the impact of natural vagaries of weather (and such) associated with production in an open system. Consequently, the process simulation is stochastic as well as fundamental; it returns a distribution of results representing the day-to-day realizations from natural variability. It also expresses day-to-night variation in the cycling solar energy. The simulation is then used to optimize pond design and management (the growth time, raceway depth, pH control, etc.) to improve profitability. Since the simulation is stochastic, nonlinear, and with multiple optima, a multiplayer direct search optimization technique with steady state convergence criteria is used for optimization. Conclusions are that (1) accounting for natural variation in the optimization leads to noticeable improvement in profitability, (2) sensitivity analysis of the model reveals where fundamental science research is needed to underpin critical techno-economic phenomena, and (3) the stochastic optimization approach has wide ranging applicability.