The　fact　that　high-strength　concrete　is　easily　to　crack　has　a　significant　negative　impact　on　its　durability　and　strength.　This　paper　gives　an　optimum　design　method　of　high-strength　concrete　for　improving　crack　resistance　based　on　orthogonal　test　artificial　neural　networks　(ANN)　and　genetic　algorithm.　First,　orthogonal　test　is　operated　to　determine　the　influence　of　the　concrete　mix　proportion　to　the　slump,　compressive　strength,　tensile　strength,　and　elastic　modulus,　followed　by　calculating　and　predicting　the　concrete　performance　using　ANN.　Based　on　results　from　orthogonal　test　and　ANN,　a　functional　relationship　among　slump,　compressive　strength,　tensile　strength,　elastic　modulus,　and　mix　proportion　has　been　built.　On　this　basis,　using　the　widely　used　shrinkage　and　creep　models,　the　functional　relationship　between　the　concrete　cracking　risk　coefficient　and　the　mix　proportion　is　derived,　and　finally　genetic　algorithm　is　used　to　optimize　the　concrete　mix　proportion　to　improve　its　crack　resistance.　The　research　results　showed　that,　compared　with　the　control　concrete,　the　cracking　risk　coefficient　of　the　optimized　concrete　was　reduced　by　25%,　and　its　crack　resistance　was　significantly　improved.