  We introduce modifications to Monte Carlo simulations of the Feynman path
integral that improve sampling of localised interactions. The new algorithms
generate trajectories in simple background potentials designed to concentrate
them about the interaction region, reminiscent of importance sampling. This
improves statistical sampling of the system and overcomes a long-time
"undersampling problem" caused by the spatial diffusion inherent in Brownian
motion. We prove the validity of our approach using previous analytic work on
the distribution of values of the Wilson line over path integral trajectories
and illustrate the improvements on some simple quantum mechanical systems
