Vegetation phenology is used in the study of climate change because of stable distribution, and ease of observation and interpretation. In this study, a 15-day time series of averaged normalized difference vegetation indices (NDVI) derived from the daily Global Inventory Modeling and Mapping Studies (GIMMS) NDVI dataset and 16-day averaged NDVI values derived from the daily Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI dataset were used to analyze trends in vegetation phenology. Firstly, using the ENVI tool to cut remote sensing data, NDVI time series data for the study area from 1982 to 2014 were obtained. Secondly, using a Savitzky-Golay filter, noise contamination caused by random factors was reduced by producing a smooth NDVI curve. Thirdly, using double logistic fitting, three important phenological parameters, including the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS) were extracted for different vegetation types. Temporal change trends, as well as its spatial distribution characteristics, of the nine major vegetation types in the Qilian Mountains were analyzed. These types included meadow, steppe, desert vegetation, shrub vegetation, alpine vegetation, coniferous forest, broad-leaved forest, cultivated vegetation, and swamp vegetation. In addition, the effects of climatic factors on phenology were analyzed by correlation analysis. The results showed that:(1) The annual variation of SOS and EOS of different vegetation types in the Qilian Mountains fluctuated in its advance or or delay; the maximum variation was observed in the swamp vegetation. The LOS of meadow, shrub, coniferous forest, and cultivated vegetation was longer, but the LOS of desert vegetation shortened. (2) The SOS of vegetation was primarily in May; the growing season of the broad-leaved forests began the earliest and desert vegetation began the latest. The EOS of vegetation occurred primarily in September; the growing season of cultivated vegetation ended earlier, whereas the growing season of the desert and swamp vegetation ended later. The LOS of vegetation was from 110 days to 140 days, among which the LOS of broad-leaved and coniferous forests was longer, whereas the LOS of desert and alpine vegetation was shorter. (3) The spatial distribution of variation trends for vegetation phenology indicated that SOS and LOS were advanced or delayed but not substantially, and the LOS was primarily shortened or prolonged, but not substantially. (4) The correlation of phenological metrics and climatic factors indicated that the accumulation of early stage temperature was beneficial to the growth of vegetation, but the amount of precipitation in March was also important to the SOS of vegetation. The EOS of different vegetation types was related with temperature in August and September, and correlated with precipitation in October and November, but the correlation was not significant. At different altitudes, the phenological parameters were different. In particular, the LOS appeared to shorten with increasing elevation, which is consistent with the spatial distribution of phenological parameters. These results explain the relationship between climate change and phenological phases. Climate change has an obvious effect on vegetation phenological parameters, and the phenological period can explicitly indicate the climate change.