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In this paper, swarm and evolutionary algorithms have been applied for the design of digital filters. Particle swarm optimization (PSO) and differential evolution particle swarm optimization (DEPSO) have been used here for the design of linear phase finite impulse response (FIR) filters. Two different fitness functions have been studied and experimented, each having its own significance. The first study considers a fitness function based on the passband and stopband ripple, while the second study considers a fitness function based on the mean squared error between the actual and the ideal filter response. DEPSO seems to be promising tool for FIR filter design especially in a dynamic environment where filter coefficients have to be adapted and fast convergence is of importance.