Sampling for Digital Soil Mapping is an expensive and time-constrained operation. It is crucial to consider these limitations in practical situations, particularly when dealing with large-scale areas that are remote and poorly accessible. To address this issue, several authors have proposed methods based on cost constraints optimization to reduce the travel time between sampling sites. These methods focused on optimizing the access cost associated to each sample site, but have not explicitly addressed field work time required for the whole sampling campaign. Hence, an estimation of fieldwork time is of great interest to assists soil surveyors in efficiently planning and executing optimized field surveys. The goal of this study is to propose, implement and test a new method named Multi-Objective Operational Sampling (MOOS), to minimize sampling route time, while ensuring that sample representativeness of the area is maintained. It offers multiple optimal sampling designs, allowing practitioners to select the most suitable option based on their desired sample quality and available time resources. The pro- posed sampling method is derived from conditioned Latin Hypercube sampling (cLHS) that optimizes both total field work time (travel time and on-site sampling time) and sample representativeness of the study area (cLHS objective function). The use of a multi-objective optimization algorithm (NSGA II) provides a variety of optimal sampling designs with varying sample size. The sampling route time computation is based on an access cost map derived from remote sensing images and expert annotation data. A least-cost algorithm is used to create a time matrix allowing precise evaluation of the time required to connect each pair of sites and thus determine an optimal path. The proposed method has been implemented and tested on sampling for pHH2 O mapping within a 651 points kilometric grid in the northern part of Saudi Arabia, where soil analyses were conducted over a 1,069