Simulated evolution (SimE) is a sound stochastic approximation algorithm based on the principles of adaptation. If properly engineered it is possible for SimE to reach near-optimal solutions in less time than simulated annealing. Nevertheless, depending on the size of the problem, it may have large run-time requirements. One practical approach to speed up the execution of the SimE algorithm is to parallelize it. This is all the more true for multi-objective cell placement, where the need to optimize conflicting objectives (interconnect wirelength, power dissipation, and timing performance) adds another level of difficulty. In this paper a distributed parallel SimE algorithm is presented for multiobjective VLSI standard cell placement. Fuzzy logic is used to integrate the costs of these objectives. The algorithm presented is based on random distribution of rows to individual processors in order to partition the problem and distribute computationally intensive tasks, while also efficiently traversing the complex search space. A series of experiments are performed on ISCAS-85/89 benchmarks to compare speedup with serial implementation and other earlier proposals. Discussion on comparison with parallel implementations of other iterative heuristics is included.