Very High Resolution (VHR) optical Remote Sensing Images (RSI) are often corrupted by noise. Among popular denoising methods in the state-of-the-art, Non Local Bayes (NLB) has led to successful results on real datasets, with high quality and reasonable computation time. However, its computation time remains prohibitive with respect to requirements of operational RSI pipelines such as Pléiades one. In this paper, we tackle such an issue and introduce several optimizations aiming to significantly reduce the computation time required by NLB while keeping the best denoising quality (i.e. preserving edges, textures, homogeneous areas). More precisely, our improvements consist of reducing multiple estimations of a same pixel with a masking technique, and modifying the spatial extent of the similar patch search area (i.e. one of the main part of non local algorithms such as NLB). We report several experiments and discuss optimal settings for these parameters, allowing a gain in computation time of 50% (resp. 15%) with optimized masking strategy (resp. spatial extent of the search area). When both contributions are combined, we achieve the same denoising quality as standard NLB while doubling the computation efficiency, the latter being increased fivefold if we accept a very small (lower than 0.1%) loss in quality.