Aiming at the problems that Symbiotic Organisms Search （SOS） algorithm only can solve single tasks and negative information transfer affects Multi-Task Optimization （MTO） performance， an Information Transfer Multi-Task SOS （ITMTSOS） algorithm was proposed. Firstly， based on multi-population evolution framework MTO， multiple populations were set according to the number of tasks. Secondly， each population ran basic SOS algorithm independently， and by introducing individual itself optimal experience and neighborhood optimal individuals， the knowledge module containing the above two was formed and transferred to the process of individual evolution when a population stagnated for several consecutive generations. Finally， the time and space complexity of ITMTSOS was analyzed. Simulation results show that ITMTSOS converges rapidly to the global optimal solution 0 when resolving a batch of different shape high-dimensional functions， and the average running time is reduced around 25.25% when compared with single task SOS； when solving the multi-dimensional 0/1 knapsack problems and the teacher-student matching problems concurrently， the optimal fitnesses on weing1 and weing7 test sets are increased by 22 767 and 22 602 respectively compared with the current published optimal results， the absolute values of the optimal and the average matching difference of teacher-student matching problem are decreased by 26 and 33 respectively， and the average running time is reduced around 7.69%.