Least-cost path pipeline route optimization based upon composite geocost surfaces has proven to be an effective and practical tool in challenging deepwater projects with complicated seafloor geology. Among its strengths are the fact that least-cost path routing offers a reproducible logical framework that is well suited to sensitivity analysis and refinement as new and more detailed data become available. At least three major kinds of uncertainty enter into the calculations-bathymetric uncertainty of seafloor digital elevation models and their derivative products, locational (geologic) uncertainty regarding the sizes and locations of mapped seafloor features and hazards, and cost uncertainty regarding the weighted relative costs assigned to seafloor features and hazards. Because these different kinds of uncertainties can be difficult to quantify and they likely interact in ways that are difficult to foretell, one pragmatic approach is to use resampling-based stochastic conditional simulation of composite geocost surfaces to assess the sensitivity of calculated routes to input data uncertainties. Characteristic dimensions of seafloor features or geohazards can be used to estimate the number of random points necessary to characterize major or first-order features known with confidence; smaller features are then simulated using random distributions with the same statistical properties and spatial correlation structures of the sampled points. The final result is not a single route, but rather a cloud of routes defining a most likely route corridor for further assessment and route refinement.