This work presents an application of Surrogate-Based Optimization (SBO) to the multipoint constrained design of the 3D DPW wing [1]in viscous transonic flow conditions.The geometry is parameterized by a control box with 36 design variables.An adaptive sampling technique focused on the optimization problem, the Intelligent Estimation Search with Sequential Learning (IES-LS), is applied.The selected SBO approach is based on the use of Support Vector Machines (SVMs) as the surrogate model for estimating the objective function, in combination with an evolutionary algorithm (EA) to enable the discovery of global optima.The aim of this work is to complement a previous one [2] by adding a study of the capability of this method to obtain an improvement for this multipoint constrained three-dimensional test case.