In this work, we have extended MDM for general functionalities by improving the optimization algorithm. The present MDM separates two coupled optimization problems for the molecular species and equilibrium geometry; MDM gradually and iteratively changes the molecular species and equilibrium geometry during the exploration of chemical space but avoids evaluating the contribution of the geometry change in the gradient. This allows for a dramatic reduction in the computational cost. This type of approach has been demonstrated in discrete optimization using semiempirical AM1-based LCAP. Moreover, we achieved rapid design by improving the constraint optimization method for updating molecular species. The proposed MDM was successfully applied to molecular design for electric dipole moment and atomization energy with small chemical spaces. The electric dipole moment is important for optical properties and intermolecular interactions. The atomization energy is related to the stability of molecules. We adopted alchemical perturbation density functional theory (APDFT) as the quantum alchemy method. The electric dipole moment is less investigated in APDFT, and it is worth examining its accuracy. We have assessed the applicability of APDFT for estimating the electric dipole moment.