Partitioned DM (deadline-monotonic) scheduling of sporadic real-time tasks is a classic research problem. This study proposes a partitioned scheduling algorithm PDM-FFD (partitioned deadline-monotonic first-fit decrease) with higher processor utilization for constrained-deadline sporadic tasks. In PDM-FFD, firstly tasks are sorted in non-decreasing order according to the relative deadline, then the first-fit strategy is utilized to select the processor core to allocate tasks, and each core adopts DM scheduling policy. Finally, a tighter schedulability determination method is obtained by analyzing the task interference time to determine the task schedulability. This study proves that the speedup factor of PDM-FFD is 3 - (3△ + 1)/(m + △) and the time complexity is O(n²) + O(nm). △= ∑_τj∈τC_j×u_j/D_max where τ_j belongs to the task set τ, C_j is the worst-case execution time, u_j is the utilization, D_max is the maximum relative deadline, n is the task number, and m is the processor core number. The speedup factor of PDM-FFD is strictly less than 3 - 1/m, which outperforms the existing multi-core partitioned scheduling algorithm FBB-FFD. Experiments show that PDM-FFD improves processor utilization by 18.5% compared to other available algorithms on a four-core processor. The PDM-FFD performance improves with the increasing processor core number, task set utilization, and task number. Due to high performance, PDM-FFD can be widely utilized in typical real-time systems such as resource-constrained spacecraft, autonomous vehicles, and industrial robots.