Biosurfactants, synthesised by microorganisms, are surface-active compounds capable of reducing surface tension and increasing system’s emulsification. Several factors, such as the use of waste instead of synthetic substrate, can influence biosurfactant production. Hence, modelling and optimization are extremely important to find an economic route for its application in industrial scale.The classical numerical methods based on gradient usually fail to obtain nhe optimum kinetic parameters because they often converge to local minima.Stochastic global search algorithms, such as the Genetic Algorithm (GA), have been showing a great potential to detect optimal solutions in complex systems as bioprocesses. This work aims to evaluate the procedure that employs GA for estimating the kinetic parameters involved in biosurfactant production from agro-industrial waste using Bacillus subtilis. Three different models were proposed to describe biomass growth, substrate consumption, biosurfactant synthesis and dissolved oxygen in the medium. The technique’squality was evaluated from the normalized sum of squared errors (SSE) and correlation coefficient (R2),calculated by the software MATLAB 2017a for each model. Two of the tested models have to be considered to achieve the optimal solution, once both presented are remarkable performance reproducing the dynamics of most variables, obtaining R² values superior to 0.9 and normalized SSE near to 0 Ver menos