Deep bed filtration (DBF) is a very dynamic, complex process involving the capture and release of fine particles within a porous medium, dependent upon the chemistry state of the liquid phase. This paper presents a general model for permeability loss within a DBF process and then applies the model to the specific system of secondary oil recovery. The use of a genetic algorithm (GA) allows for the determination of the model parameters based on an efficient set of experimental data. GAs are optimization algorithms, based on the mechanism of natural selection. The explanation, benefits and development of the GA coding are included. The results found model the process with > 97% accuracy for permeability loss up to 90%. The GA format allows for easy adaptation of this model to represent other filtration systems and further illustrates the usefulness of GAs in transport phenomena.