The transportation network design problem (NDP) is a high capital investment decision-making problem that inherently involves both subjective and objective uncertainties as well as multiple objectives. Goal programming is a practically useful approach with an explicit consideration of planners’ goal setting and priority structure among the multiple objectives. This paper describes the development of a hybrid goal programming (HGP) approach for modeling both subjective and objective uncertainties simultaneously in the NDP decision-making process. Planners’ subjective uncertainty regarding the linguistic setting of goals and priority structure is characterized as a set of fuzzy variables with nonlinear achievement and satisfaction functions, and the objective travel demand uncertainty is characterized as a set of random variables with predefined probability distributions. The HGP-NDP is formulated as a chance-constrained model in a bi-level programming framework and solved by a genetic algorithm procedure based on random simulation and fuzzy evaluation. The paper provides numerical examples and a real case study to demonstrate the features and applicability of the proposed HGP approach in solving the NDP under an uncertain environment.