I will discuss a new Monte Carlo method for efficient sampling of trajectories with fixed initial and final conditions in a system with discrete degrees of freedom. The method can be applied to any stochastic process with local interactions, including systems that are out of equilibrium. Combining the proposed path-sampling algorithm with a suitable thermodynamic integration, one can compute transition rates between metastable states. To demonstrate the performances of the method and compare with other algorithms, I will discuss the well studied 2D Ising model with periodic boundary conditions.