A near-global optimization approach is proposed to design blending recipes for refinery products under uncertainties. In the refining industry, the most valuable products, such as gasoline and diesel, are produced by blending several intermediate feedstocks to maximize profit and ensure that all qualities are on specification. Due to the presence of property uncertainties, optimal blending recipes under linear mixing laws should be designed by solving a linear program (LP) with joint chance-constraints. However, joint chance-constrained (JCC) programming is generally intractable even with Gaussian distributions and thereby it is usually converted to an individual chance-constrained (ICC) program to achieve a conservative approximation. In order to reduce this conservatism, we find a global optimal solution for the ICC program. In case studies, a multi-product blending problem with 12 chance constraints and a crude oil procurement example with 14 chance constraints are studied to test and compare the proposed scheme with start-of-the-art optimization software to demonstrate its superior performance in terms of computational time.