This paper proposes a new evaluation strategy for product-based possibilistic networks learning algorithms. The proposed strategy is mainly based on sampling a possibilistic networks in order to construct an imprecise data set representative of their underlying joint distribution. Experimental results showing the efficiency of the proposed method in comparing existing possibilistic networks learning algorithms is also presented.