Rising fuel cost has motivated increased fuel efficiency for freight trains. At cruising speed,the largest contributing factor to the fuel consumption is aerodynamic drag. As a result of stagnationand flow separation on and around lead and trailing cars, the first and last railcars experiencegreater drag than intermediate cars. Accordingly, this work focused on reducing drag on lead locomotivesby designing and optimizing an add-on nose fairing that is feasible for industrial operation.The fairing shape design was performed via computational fluid dynamic (CFD) software.The simulations consisted of two in-line freight locomotives, a stretch of rails on a raised subgrade,a computational domain, and a unique fairing geometry that was attached to the lead locomotive ineach non-baseline case. Relative motion was simulated by fixing the train and translating the rails,subgrade, and ground at a constant velocity. An equivalent uniform inlet velocity was applied atzero degree yaw to simulate relative motion between the air and the train.Five fairing families-Fairing Families A-E (FFA-FFE)-are presented in this thesis.Multidimensional regressions are created for each family to approximate drag as a function ofthe design variables. Thus, railroad companies may choose an alternative fairing if the recommendedfairing does not meet their needs and still have a performance estimate. The regression forFFE is used as a surrogate model in a surrogate based optimization. Results from a wind tunneltest and from CFD are reported on an FFE geometry to validate the CFD model. The wind tunneltest predicts a nominal drag reduction of 16%, and the CFD model predicts a reduction of 17%.A qualitative analysis is performed on the simulations containing the baseline locomotive, the optimalfairings from FFA-FFC, and the hybrid child and parent geometries from FFA & FFC. Theanalysis reveals that optimal performance is achieved for a narrow geometry from FFC becausesuction behind the fairing is greatly reduced. Similarly, the analysis reveals that concave geometriesboost the flow over the top leading edge of the locomotive, thus eliminating a vortex upstreamof the windshields. As a result, concave geometries yield greater reductions in drag.The design variable definitions for each family were strategically selected to improve manufacturability,operational safety, and aerodynamic performance relative to the previous families.As a result, the optimal geometry from FFE is believed to most completely satisfy the constraintsof the design problem and should be given the most consideration for application in the railroadindustry. The CFD solution for this particular geometry suggests a nominal drag reduction of 17%on the lead locomotive in an industrial freight train.