This　paper　presents　a　reliability-based　design　optimization　method　for　bridge　structures,　considering　the　uncertainties　of　the　material　parameters　and　the　effect　of　bridge-vehicle　interaction.　The　uncertainties　of　system　parameters,　such　as　Young＇s　modulus　and　mass　density,　are　considered,　which　are　simulated　as　Gaussian　and/or　lognormal　random　fields.　The　formulas　used　to　represent　the　random　fields　of　material　properties　and　system　matrices　are　derived.　The　Gaussian　random　inputs　are　approximated　with　Karhunen-Loeve　(KL)　expansion,　while　the　lognormal　random　inputs　are　approximated　with　a　combination　of　KL　expansion　and　Polynomial　Chaos　(PC)　expansion.　Reliability-based　design　optimization　analysis　is　conducted　to　determine　the　minimum　required　crosssection　area　of　bridge　structures　under　probability　constraints,　in　which　the　failure　probability　is　estimated　from　the　Kriging　surrogate　model　and　Monte　Carlo　Simulation　(MCS)　method.　Numerical　studies　on　a　simply　supported　beam　under　a　moving　force　and　a　three-dimensional　box-section　bridge　under　a　moving　vehicle　are　conducted　to　investigate　the　efficiency　and　accuracy　of　the　proposed　approach.　The　reliability　analysis　results　are　compared　with　those　obtained　from　MCS　method,　validating　the　accuracy　of　the　proposed　approach.　With　the　proposed　method,　the　minimum　value　of　cross-section　area　of　the　bridge　under　the　preset　probability　constraint　can　be　obtained.