As "a new frontier in evolutionary computation research", evolutionary transfer optimization(ETO) will overcome the traditional paradigm of zero reuse of related experience and knowledge from solved past problems in researches of evolutionary computation. In scheduling applications via ETO, a quite appealing and highly competitive framework "meeting" between them could be formed for both intelligent scheduling and green scheduling, especially for international pledge of "carbon neutrality" from China. To the best of our knowledge, our paper on scheduling here, serves as the 1st work of a class of ETO frameworks when multiobjective optimization problem "meets" single-objective optimization problems in discrete case (not multitasking optimization). More specifically, key knowledge conveyed for industrial applications, like positional building blocks with genetic algorithm based settings, could be used via the new core transfer mechanism and learning techniques for permutation flow shop scheduling problem(PFSP). Extensive studies on well-studied benchmarks validate firm effectiveness and great universality of our proposed ETO-PFSP framework empirically. Our investigations (1) enrich the ETO frameworks, (2) contribute to the classical and fundamental theory of building block for genetic algorithms and memetic algorithms, and (3) head towards the paradigm shift of evolutionary scheduling via learning by proposal and practice of paradigm of "knowledge and building-block based scheduling" (KAB2S) for "industrial intelligence" in China.