Multi-modal biometrics has numerous advantages over unimodal biometric systems. Decision level fusion is the most popular fusion strategy in multimodal biometric systems. Recent research has shown promising performance of hand based biometrics, i.e. palmprint and hand geometry over other biometric modalities. However, the improvement in performance is constrained by the lack of optimal sensor points and fusion strategy. In this paper, we have implemented a particle swarm based optimization technique for selecting optimal parameters through decision level fusion of two modalities: palmprint and hand geometry. The experimental evaluation on a database of 100 users confirms the utility of the decision level fusion using particle swarm optimization.