The aerospace industry is on a perpetual drive to optimize structures with developments in modeling and analysis capabilities. The traditionally used laminates with 0°,90°,±45° orientations are called Legacy QUAD Laminates (LQL). The recent discovery of Trace of an orthotropic stress tensor A allows reduction of design variables by the replacement of LQL with equivalent stiffness DD¹ (double-double) laminate having self-repeating orientations in a set of ±ϕ/±ψ. The optimized LQL structures are with mid-plane symmetry, excessive ply-migrations, and variability in ply-orientations, which make the manufacturing process cumbersome. On the other hand, DD-laminates are simplified, thinner and free from mid-plane symmetry, thus enable ten-fold reduction in production resources. The present study, an artificial intelligent (AI) genetic-algorithm based stochastic optimizer replaces LQL with DD-laminates, which follows DD-drop design for mass optimization. The optimization algorithm works with unit-circle failure, buckling mode, and wing-tip deflection design criteria and derives optimal-wing with lowest mass, well suited for design requirements in multiple design load-case. The application of algorithm shows 68-70% mass reduction to an initial full-length ply wing-box model of LQL. The minimization of ply-migrations by D/DD-drop optimization yields structures with better resistance for delamination and well suited for automated production.