Nonlinear bioreactors are considered essential technology in chemical and biochemical industries. This paper presents a proposal of a robust model based fault diagnosis in a nonlinear bioreactor, formulated as the solution of an inverse problem. The optimization problem is solved by using four different evolutionary strategies: Particle Swarm Optimization (PSO), Differential Evolution (DE), Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and Particle Swarm Optimization with Memory (PSO-M), with DE resulting the best according to the evaluated quantitative indicators. The results obtained with this approach indicate advantages in comparison to other methods of fault diagnosis (FDI) present in literature. (C) 2016 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.