This　paper　presents　a　genetic　algorithm　(GA)　approach　for　parameters　optimization　of　support　vector　machine,　which　is　used　for　the　object-oriented　classification　of　high　spatial　resolution　images　over　urban　area.　The　proposed　method　is　a　three-step　routine　involves　the　integration　of　1)　image　segmentation,　2)　GA-based　parameter　optimization　of　Support　vector　machine　(SVM),　and　3)　objected-based　classification.　Experiments　conducted　on　multi-spectral　Quick-Bird　image　fused　with　panchromatic　image　in　Fuzhou　city.　In　addition,　a　traditional　parameter　searching　method,　Grid　algorithm,　was　investigated　to　evaluate　the　effectiveness　of　the　proposed　approach.　The　results　show　that　our　proposed　GA-based　approach　significantly　outperforms　the　Grid　algorithm　both　in　terms　of　classification　accuracy　and　time　efficiency.　©　2011　IEEE.