Current surrogate formulation methods usually adopt distillation profiles, density, viscosity, surface tension, molecular weight, research/motor octane number (RON/MON), cetane number (CN), heating value, H/C ratio, and threshold sooting index (TSI) as target properties, but these parameters are most likely unavailable for new fuel molecules and mixtures at the early stage of fuel development. A novel functional group contribution method (GCM) based on accurate fuel compositions is proposed to formulate surrogates effectively and quickly. This method can successfully replicate the density, sound speed, kinematic viscosity, ignition delay times, and speciations of POSF 4658, rapeseed methyl ester, diesel, and fuels for advanced combustion engines (FACE) C gasoline under a broad range of conditions (phi = 0.37-2.0, T-init = 500-1600 K, P-init = 1-20 atm), and its predictive capacity is superior to that of traditional methods in most cases. Fuel properties would match automatically between a surrogate and target fuel if the discrepancies of functional groups are minimized. Three important factors contribute to its high reproducibility: first, GCM captures the complicated dependence of fuel physical/chemical properties on the fuel molecular structure and functional groups; second, it correctly assumes that fuel physical and chemical properties are a sum result of the fuel molecular structure and functional groups; third, the functional group interactions and their effect on fuel reactivity are considered in the functional group classification system. The GCM can not only formulate in the normal direction from a complex target fuel to a simple surrogate fuel but also enable starting from a simple target fuel toward a complex surrogate fuel during fuel design in the refinery industry.