The multiobjective flowshop problem with makespan and total flow time as objectives is addressed. A genetic local search algorithm is proposed with the ability to allocate the computational resources through the dynamic population size and local search intensity. The proposed method is compared with existing algorithms for flowshop scheduling with a public benchmark problem set. The experimental results show that the proposed method is capable of discovering solutions with better quality and diversity. The proposed method yields the best known nondominated solutions for the commonly studied permutation flowshop benchmarks, and the set of best known solutions is useful for the evaluation of performance of future studies.