The invention discloses a big data-based optimization method of a ground public-transportation fixed-line timetable. First, all collinear public-transportation lines are extracted for target lines, which need to be studied, according to GPS trajectory data, IC card swiping data of the collinear public-transportation lines and number data of vehicles arriving at each station in unit time are extracted, and vehicle running time data are extracted; then all the extracted data are analyzed and processed; passenger preference coefficients are calculated on the basis of passenger flow data, full-dayworking time periods of the public-transportation lines, maximum passenger capacity of the public-transportation service vehicles, standard full-load rates, first/last-vehicle departure time, and maximum and minimum vehicle departure intervals are determined; and then the processed data are input into a constructed multi-objective optimization model to obtain an optimized public-transportation vehicle departure timetable. The optimization method of the ground public-transportation fixed-line timetable provided by the invention can satisfy passenger flow demand, enhance public-transportation operation efficiency, reduce public-transportation travel delay, improve service reliability, and improve passenger satisfaction degrees.