Sampling design plays a key role in digital soil mapping (DSM). Efficient sampling design for multiple soil properties is increasingly needed for multivariate soil survey and mapping. However, most of the present sampling methods are not developed for multiple soil properties. Different soil properties have different influential covariates, but usually only one set of covariates is used in designing samples for multiple soil properties which makes simultaneously mapping multiple soil properties accurately difficult. This paper proposed a multiple soil properties oriented representative sampling strategy (MPRS) by considering the influential environmental covariates for each soil property. The method first selects the most influential set of environmental covariates for each soil property, then uses fuzzy c-means (FCM) clustering to generate environmental clusters relating to spatial variation patterns for each soil property, and the selected samples are representative of as many typical locations of environmental clusters for multiple soil properties as possible. The proposed sampling method was applied for mapping soil sand content and soil organic matter content at surface (0–20 cm) and subsurface (20–40 cm) layers in a study area with 5900 km² located in Anhui Province, China, and compared with two methods, the purposive sampling (PS) method and integrative hierarchical stepwise sampling (IHS) method. The results showed that the proposed sampling method achieved the most accurate prediction for most of the four soil properties over different sample sizes. The proposed sampling method also has an advantage to extract representative samples which can better cover multiple soil properties with a limit of a small sample size. On average, the improvement of prediction accuracy by using the MPRS method was 38.1% and 36.3% compared with PS and IHS in terms of ${\mathrm{R}}^2$, 4.8% and 4.6% in terms of RMSE, and 11.7% and 13.7% in terms of CCC, respectively. Our case study confirmed the necessity to consider the difference of the influential environmental variable combinations for the multiple soil properties oriented sampling design. We conclude that MPRS is a potential effective method for supporting DSM for multiple soil properties.