This paper present a new variance reduction technique called Super Voxel Woodcock (SVW), which combines Woodcock tracking technique with the super voxel concept, used in computer graphics. It consists in grouping the voxels of the volume in a super voxel grid (pre-processing step) by associating to each of the super voxels a local value of the most attenuate medium which will later serve to the interaction distances sampling. SVW allows reducing the sampling of the particle path while a high-density material is present within the simulated phantom. In order to evaluate the performance of the SVW method compare to both standard and woodcock tracking methods, algorithms were implemented within the same GPU MCS framework GGEMS. A MCS of prostate brachytherapy was used as benchmark, showing that SVW method provide the same dosimetry results than the Woodcock tracking method or the standard particle navigation approach. Calculation of the efficiency factor has shown that the proposed method improves significantly the performance of the Woodcock tracking method by a factor of 2.7. The next step will consist to estimate the improvement of this method considering different medical applications context.