Unimodal biometric systems that are based on utilising a single biometric trait often face limitations that influence their performances. In this paper, a proposed fusion system of three biometrics at the feature level based on Particle Swarm Optimization approach (PSO) is presented. A new multi objective fitness function for PSO has been used. This function has three main objectives, maximize the between-class scatter among the different classes, minimize the within-class scatter in the same class and improve the recognition rate of the system. Results shown how the optimized system fused at feature level can improve the recognition rate, reduce the number of features, reduce the total equal error rates and finally decrease the time consumed in recognition to the half.