A system of experimental design is outlined that attempts to encompass many of the major work in factorial experimental design of the century. The system has four broad branches: (i) regular orthogonal designs,(ii) nonregular orthogonal designs, (iii) response surface designs, (iv) optimal designs. Regular orthogonal designs include the 2^{n-k} and 3^{n-k} designs. Major issues are optimal assignment of factors and interactions via the minimum aberration and related criteria. The problem becomes harder if the factors cannot be treated symmetrically (e.g., blocking or split-plot structure, and robust parameter designs.) Nonregular orthogonal designs were traditionally used for factor screening and main effect estimation. They have been shown to possess some hidden projection property that allows interactions among a smaller number of factors to be estimated. Response surface designs are used primarily for exploring parametric surfaces, while optimal designs are chosen to optimize a given criterion based on a specified model. Recent work shows that many nonregular designs can be used to screen a large number of factors as well as efficiently estimate a quadratic response surface on projected designs. This shows that the boundary between (ii) and (iii) is getting blurred. If time permits, potential applications to marketing research and bioinformatics will be mentioned.