Connectivity analysis characterizes normal and altered brain function, for example, using the phase lag index (PLI), which is based on phase relations. However, reliability of PLI over time is limited, especially for single- or regional-link analysis. One possible cause is repeated changes of network configuration during registration. These network changes may be associated with changes of the surface potential fields, which can be characterized by microstate analysis. Microstate analysis describes repeating periods of quasistable surface potential fields lasting in the subsecond time range. This study aims to describe a novel combination of PLI with microstate analysis (microstate-segmented PLI = msPLI) and to determine its impact on the reliability of single links, regional links, and derived graph measures. msPLI was calculated in a cohort of 34 healthy controls three times over 2 years. A fully automated processing of electroencephalography was used. Resulting connectomes were compared using Pearson correlation, and test-retest reliability (TRT reliability) was assessed using the intraclass correlation coefficient. msPLI resulted in higher TRT reliability than classical PLI analysis for single or regional links, average clustering coefficient, average shortest path length, and degree diversity. Combination of microstates and phase-derived connectivity measures such as PLI improves reliability of single-link, regional-link, and graph analysis.