This paper considers the flowline manufacturing cell scheduling problem (FMCSP) with sequence-dependent family setup times (SDFSTs) for total tardiness and mean total flowtime minimization. Based on the mathematical model of this problem, a hybrid harmony search (HHS) is proposed. One-point crossover operator that is commonly used in genetic algorithms is adapted and applied for diversification. Iterative local search method is used to further improve the solution. The effectiveness of HHS in finding optimal or near-optimal schedules is compared with the meta-heuristics, NSGA-II, MA and MSA, which are adapted and renamed as NSGA - IIapt, MA(apt) and MSA(apt), respectively. Experimental results from 900 problem instances show that HHS performs relatively better than these meta-heuristics for finding schedules to minimize the multi-objective FMCSP with SDFSTs. The proposed HHS algorithm also generates the maximal Pareto front among all these heuristics.