Biophysical markers of cells such as cellular electrical and mechanical properties have been proven as promising label-free biomarkers for studying, characterizing, and classifying different cell types and even their subpopulations. Further analysis or manipulation of specific cell types or subtypes requires accurate isolation of them from the original heterogeneous samples. However, there is currently a lack of cell sorting ability that could actively separate a large number of individual cells at the single-cell level based on their multivariate biophysical makers or phenotypes. In this work, we, for the first time, demonstrate label-free and high-throughput acoustic single-cell sorting activated by the characterization of multivariate biophysical phenotypes. Electrical phenotyping is implemented by single-cell electrical impedance characterization with two pairs of differential sensing electrodes, while mechanical phenotyping is performed by extracting the transit time for the single cell to pass through microconstriction from the recorded impedance signals. A real-time impedance signal processing and triggering algorithm has been developed to identify the target sample population and activate a pulsed highly focused surface acoustic wave for single-cell level sorting. We have demonstrated acoustic single-particle sorting solely based on electrical or mechanical phenotyping. Furthermore, we have applied the developed microfluidic system to sort live MCF-7 cells from a mixture of fixed and live MCF-7 population activated by a combined electrical and mechanical phenotyping at a high throughput >100 cells/s and purity ∼91.8%. This demonstrated ability to analyze and sort cells based on multivariate biophysical phenotyping provides a solution to the current challenges of cell purification that lack specific molecular biomarkers.