To develop useful drugs and materials, chemists synthesize diverse molecules by trying various reactants and reaction routes. Toward automating this process, we propose a deep generative model, called cascaded variational autoencoder (casVAE), for synthesizable molecular design. It generates a reaction tree, where the reactants are chosen from commercially available compounds and the synthesis route is constructed as a tree of reaction templates. The first part of casVAE is designed to generate a molecule called a surrogate product, while the second part constructs a reaction tree that synthesizes it. In benchmarking, casVAE showed its ability to generate reaction trees that yield high-quality and synthesizable molecules. An implementation of casVAE is publicly available at https://github.com/tsudalab/rxngenerator.