In de novo drug design, deep learning-based approaches have become essential to efficiently navigate the vast chemical space of drug-like molecules. Recently, diffusion-based models have attracted significant attention in the generation of target-binding molecules. However, these models have difficulty in simultaneously optimizing the binding affinity and drug-like properties and require high computational costs because of the long and sequential denoising process. To address these limitations, we propose the Global and local integrated gradient-based Diffusion Model (GlintDM). GlintDM introduces a significantly faster denoising process, namely skip transition, by leveraging global gradients and local gradients. Due to the fast denoising process, GlintDM can perform the following three phases during the molecule generation: position refinement, candidate evaluation, and ligand resampling. These phases allow GlintDM to identify optimal binding positions to the target protein and generate molecules satisfying multi-objective molecular properties. As a result, GlintDM outperforms other methods on both the CrossDocked and Binding MOAD datasets for Vina-related scores. Further validation through the PoseBusters test and assessment of molecular properties, such as steric clash and geometric properties, confirm that GlintDM can generate stable and high-quality molecules.