Echo　state　network　(ESN)　is　a　powerful　tool　for　nonlinear　system　modeling.　However,　the　random　setting　of　structure　(mainly　the　reservoir)　may　degrade　its　estimation　accuracy.　To　create　the　optimal　reservoir　for　a　given　task,　a　novel　ESN　design　method　based　on　differential　evolution　algorithm　is　proposed.　Firstly,　the　weight　matrix　of　reservoir　is　constructed　via　the　singular　value　decomposition　(SVD).　Then,　the　corresponding　singular　values　are　optimized　by　using　a　variant　of　differential　evolution　algorithm.　Finally,　some　comparisons　are　made　which　show　that　the　proposed　ESN　has　better　training　performance　than　other　deterministic　and　evolutionary　algorithm　based　ESNs.