In this work a study on the application of bio-inspired strategies for optimization to Fault Diagnosis in industrial systems is presented. The principal aim is to establish a basis for the development of new and viable model-based Fault Diagnosis Methods which improve some difficulties that the current methods cannot avoid. These difficulties are related mainly with fault sensitivity and robustness to external disturbances. In this study, there have been considered the Differential Evolution and the Ant Colony Optimization algorithms. This application is illustrated using simulated data of the Two tanks system benchmark. In order to analyze the advantages of these algorithms to obtain a diagnosis which needs to be sensitive to faults and robust to external disturbances, some experiments with incipient faults and noisy data have been simulated. The results indicate that the proposed approach, basically the combination of the two algorithms, characterizes a promising methodology for Fault Diagnosis.