Discovering new solid-state materials necessitates the ability to rapidly explore the vast space of crystal structures and locate stable regions. Generating stable materials with desired properties and composition is a challenging task because of (a) the exponentially large number of possibilities when the elements from the periodic table are considered along with vast variations in their 3D arrangement and corresponding lattice parameters and (b) the rarity of the stable structures. Furthermore, materials discovery requires not only optimized solution structures but also diversity in the configuration of generated material structures. Existing methods have difficulty when exploring large material spaces and generating significantly diverse samples with desired properties and requirements. We propose Crystal Hierarchical Generative Flow Network (CHGlownet), a new generative model that employs a hierarchical exploration strategy with Generative Flow Network to efficiently explore the material space while generating the crystal structure with desired properties. Our model decomposes the large material space into a hierarchy of subspaces of space groups, lattice parameters, and atoms. We significantly outperform the iterative generative methods such as Generative Flow Network (GFlowNet) and Physics Guided Crystal Generative Model (PGCGM) in crystal structure generative tasks in validity, diversity, and generating stable structures with optimized properties and requirements.