Recent hardware changes introduced on the Orbitrap Ascend Tribrid MS include dual ion routing multipoles (IRMs) that can be used to parallelize accumulation, dissociation, and mass analysis of three separate ion populations. Here we explore how this architecture improves N- and O-glycopeptide characterization by increasing scan acquisition speeds without sacrificing spectral quality. The balance between scan speed and MS/MS product ion signal-to-noise is especially important in glycoproteomics. Complexities of glycopeptide fragmentation necessitate large precursor ion populations, and consequently, long ion accumulation times, for quality MS/MS spectra. To compound matters further, dissociation methods like electron transfer dissociation (ETD) that benefit glycopeptide characterization come with overhead times that also slow down scan acquisition. Conversely, heterogeneity inherent to glycosylation means that any given retention time during an LC-MS/MS analysis may contain numerous glycopeptide species to target through data-dependent acquisition. Often duty cycle is sacrificed to some degree, which results in higher quality spectra of abundant species but leaves other precursor ions under-sampled. We analyze mixtures of N- and O-glycopeptides to show that 20-30% more MS/MS scans can be acquired when parallelizing three ion populations using the dual IRMs of the Orbitrap Ascend. This translates to 10-20% gains in glycopeptide identifications depending on dissociation type(s), scan acquisition schemes, and method parameters (e.g., precursor ion accumulation and reaction times). Focusing on O-glycopeptide analysis with ETD-based methods, we also explore how acquisition rates and ion-ion reaction times affect identifications and product ions generation. We show what parameters need to be considered in O-glycopeptide characterization to generate c- and z-type ions that can be used for O-glycosite localization while also maximizing scan acquisition rates to improve total site-localized O-glycopeptide identification. In all, we show how architectural changes to the Tribrid MS platform benefit glycoproteomic experiments by parallelizing scan functions to minimize overhead time and improve sensitivity.